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Sensors for Fault Detection

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 218475

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Special Issue Editors

Prof. Dr. Mohammed Chadli

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Guest Editor

Univ Evry Department UFR Sciences and Technologies, Université Paris-Saclay, 91020 Evry, France
Interests: fuzzy/polynomial/LPV/singular systems; LMI/SOS; FDI; FTC; automotive control; intelligent vehicle; renewable energy
Special Issues, Collections and Topics in MDPI journals

Dr. Fei Meng

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Guest Editor

Shanghai Maritme University, China
Interests: MATLAB simulation; vehicle; system modeling; mechatronics

Dr. Hui Zhang

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Guest Editor

School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nader Meskin

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Guest Editor

Qatar University, Doha, Qatar
Interests: control systems; fault diagnosis; system biology

Special Issue Information

Dear Colleagues,

Advanced fault diagnosis theory, and techniques based on data fusion, need higher-quality information from multiple types of sensor data. Different techniques for data fusion draw from a broad set of disciplines, such as statistical estimation, observer design, signal and image processing, artificial intelligence, etc., with various applications in vehicle, aerospace and robotics domain, distributed sensor network, and monitoring of manufacturing processes.

The Special Issue seeks to encourage the development of a wider variety of sensor fault diagnosis schemes for nonlinear dynamic system. The objective is to identify different types of sensor faults (bias, drift, precision degradation, etc.), particularly for incipient sensor faults. Methods like Kalman filters, observer design or other methods in the artificial intelligence community, e.g., fuzzy logic, machine learning methods, artificial neural networks, and Bayesian networks, may also find application in the domains of interest for this Special Issue.

In this Special Issue, particular emphasis should be placed on progresses in the theory of fault diagnosis based on nonlinear filtering, data fusion based on heterogeneous sensors and relevant to the field of data-driven sensor fault diagnosis, both in theory and applications.

We invite manuscripts that are original and not previously published. Topics suited for this Special Issue include, but are not limited to:

Data-driven sensor fault diagnosis
Distributed fault diagnosis
Data fusion based on nonlinear filtering
Data fusion in distributed sensor network
Techniques in multi-sensor data fusion
Data-driven estimation in nonlinear systems
Data fusion based sensors condition monitoring

Prof. Dr. Mohammed Chadli
Dr. Fei Meng
Prof. Dr. Hui Zhang
Prof. Dr. Nader Meskin
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Fault diagnosis
Data-driven sensor fault diagnosis
Distributed fault diagnosis
Data fusion in distributed sensor network
Data-driven estimation in nonlinear systems

Published Papers (43 papers)

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39 pages, 28046 KiB

Open AccessArticle

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors

by Wonbin Na, Changwoo Park, Seokjoo Lee, Seongo Yu and Hyeongcheol Lee

Sensors 2018, 18(8), 2720; https://doi.org/10.3390/s18082720 - 18 Aug 2018

Cited by 9 | Viewed by 5620

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have [...] Read more.

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 11859 KiB

Open AccessFeature PaperArticle

Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

by Michele Crispoltoni, Mario Luca Fravolini, Fabio Balzano, Stephane D’Urso and Marcello Rosario Napolitano

Sensors 2018, 18(8), 2488; https://doi.org/10.3390/s18082488 - 01 Aug 2018

Cited by 17 | Viewed by 3747

This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed [...] Read more.

This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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21 pages, 526 KiB

Open AccessArticle

Application-Aware Anomaly Detection of Sensor Measurements in Cyber-Physical Systems

by Amin Ghafouri, Aron Laszka and Xenofon Koutsoukos

Sensors 2018, 18(8), 2448; https://doi.org/10.3390/s18082448 - 27 Jul 2018

Cited by 2 | Viewed by 2847

Detection errors such as false alarms and undetected faults are inevitable in any practical anomaly detection system. These errors can create potentially significant problems in the underlying application. In particular, false alarms can result in performing unnecessary recovery actions while missed detections can [...] Read more.

Detection errors such as false alarms and undetected faults are inevitable in any practical anomaly detection system. These errors can create potentially significant problems in the underlying application. In particular, false alarms can result in performing unnecessary recovery actions while missed detections can result in failing to perform recovery which can lead to severe consequences. In this paper, we present an approach for application-aware anomaly detection (AAAD). Our approach takes an existing anomaly detector and configures it to minimize the impact of detection errors. The configuration of the detectors is chosen so that application performance in the presence of detection errors is as close as possible to the performance that could have been obtained if there were no detection errors. We evaluate our result using a case study of real-time control of traffic signals, and show that the approach outperforms significantly several baseline detectors. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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18 pages, 2621 KiB

Open AccessArticle

Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines

by Tamas Ruppert and Janos Abonyi

Sensors 2018, 18(7), 2346; https://doi.org/10.3390/s18072346 - 19 Jul 2018

Cited by 27 | Viewed by 5425

Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production. Real-time operator support and performance monitoring require accurate information on the activities of operators. The problem with tracing hundreds of activity times is critical due [...] Read more.

Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production. Real-time operator support and performance monitoring require accurate information on the activities of operators. The problem with tracing hundreds of activity times is critical due to the enormous variability and complexity of products. To handle this problem a software-sensor-based activity-time and performance measurement system is proposed. To ensure a real-time connection between operator performance and varying product complexity, fixture sensors and an indoor positioning system (IPS) were designed and this multi sensor data merged with product-relevant information. The proposed model-based performance monitoring system tracks the recursively estimated parameters of the activity-time estimation model. As the estimation problem can be ill-conditioned and poor raw sensor data can result in unrealistic parameter estimates, constraints were introduced into the parameter-estimation algorithm to increase the robustness of the software sensor. The applicability of the proposed methodology is demonstrated on a well-documented benchmark problem of a wire harness manufacturing process. The fully reproducible and realistic simulation study confirms that the indoor positioning system-based integration of primary sensor signals and product-relevant information can be efficiently utilized in terms of the constrained recursive estimation of the operator activity. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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25 pages, 11977 KiB

Open AccessArticle

Partial Inductance Model of Induction Machines for Fault Diagnosis

by Manuel Pineda-Sanchez, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Bano, Martin Riera-Guasp and Juan Perez-Cruz

Sensors 2018, 18(7), 2340; https://doi.org/10.3390/s18072340 - 18 Jul 2018

Cited by 11 | Viewed by 4206

The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models [...] Read more.

The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 1281 KiB

Open AccessArticle

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

by Vincent Judalet, Sébastien Glaser, Dominique Gruyer and Saïd Mammar

Sensors 2018, 18(7), 2332; https://doi.org/10.3390/s18072332 - 18 Jul 2018

Cited by 16 | Viewed by 4093

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly [...] Read more.

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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14 pages, 6029 KiB

Open AccessArticle

Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

by Yong Lv, Houzhuang Zhang and Cancan Yi

Sensors 2018, 18(7), 2325; https://doi.org/10.3390/s18072325 - 18 Jul 2018

Cited by 6 | Viewed by 2873

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate [...] Read more.

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 7193 KiB

Open AccessArticle

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

by Zhiqiang Liao, Liuyang Song, Peng Chen, Zhaoyi Guan, Ziye Fang and Ke Li

Sensors 2018, 18(7), 2235; https://doi.org/10.3390/s18072235 - 11 Jul 2018

Cited by 16 | Viewed by 4057

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the [...] Read more.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 5168 KiB

Open AccessArticle

UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil

by Yang Sing Leong, Pin Jern Ker, M. Z. Jamaludin, Saifuddin M. Nomanbhay, Aiman Ismail, Fairuz Abdullah, Hui Mun Looe and Chin Kim Lo

Sensors 2018, 18(7), 2175; https://doi.org/10.3390/s18072175 - 06 Jul 2018

Cited by 37 | Viewed by 9343

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored [...] Read more.

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index (CI) of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of CI of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the CI of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the CI of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and CI of the oil shows higher accuracy with root mean square error (RMSE) of 0.1961. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 2418 KiB

Open AccessArticle

LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

by Donghyun Park, Seulgi Kim, Yelin An and Jae-Yoon Jung

Sensors 2018, 18(7), 2110; https://doi.org/10.3390/s18072110 - 30 Jun 2018

Cited by 100 | Viewed by 9858

Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle [...] Read more.

Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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17 pages, 3047 KiB

Open AccessArticle

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

by Xinan Chen, Zhipeng Wang, Zhe Zhang, Limin Jia and Yong Qin

Sensors 2018, 18(7), 2097; https://doi.org/10.3390/s18072097 - 29 Jun 2018

Cited by 29 | Viewed by 4337

Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is often [...] Read more.

Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is often laborious and costly in practice, whereas there are abundant unlabeled samples which also imply health information of bearings. Thus, it is worthwhile to develop semi-supervised methods of fault diagnosis to make effective use of the plentiful unlabeled samples. Nevertheless, considering the normal data are much more than the faulty ones, the problem of imbalanced data exists among unlabeled samples for fault diagnosis. Besides, in practice, bearings often work under uncertain and variable operation conditions, which would also have negative influence on fault diagnosis. To solve these issues, a novel hybrid method for bearing fault diagnosis is proposed in this paper: (1) Inspired by visibility graph, a novel fault feature extraction method named visibility graph feature (VGF) is proposed. The obtained features by VGF are natively insensitive to variable conditions, which has been validated by a simulation experiment in this paper; (2) On basis of VGF, to deal with imbalanced unlabeled data, graph-based rebalance semi-supervised learning (GRSSL) for fault diagnosis is proposed. In GRSSL, a graph based on a weighted sparse adjacency matrix is constructed by the k-nearest neighbors and Gaussian Kernel weighting algorithm by means of the samples. Then, a bivariate cost function over classification and normalized label variable is built up to rebalance the importance of labels. Finally, the proposed VGF-GRSSL method was verified by data collected from Case Western Reserve University Bearing Data Center. The experiment results show that the proposed method of bearing fault diagnosis performs effectively to deal with the imbalanced unlabeled data under variable conditions. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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23 pages, 6338 KiB

Open AccessArticle

Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models

by Alexander E. Prosvirin, Manjurul Islam, Jaeyoung Kim and Jong-Myon Kim

Sensors 2018, 18(7), 2040; https://doi.org/10.3390/s18072040 - 26 Jun 2018

Cited by 20 | Viewed by 4534

The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze [...] Read more.

The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze rub-impact faults. However, traditional EMD suffers from “mode-mixing”, and in both EMD and EEMD the relevance of the extracted components to rubbing processes must be determined. In this paper, we introduce a new informative intrinsic mode function (IMF) selection method for EEMD and a hybrid feature model for diagnosing rub-impact faults of various intensities. Our method uses a novel selection procedure that combines the degree-of-presence ratio of rub impact and a Kullback–Leibler divergence-based similarity measure into an IMF quality metric with adaptive threshold-based selection to pick the meaningful signal-dominant modes. Signals reconstructed using the selected IMFs contained explicit information about the rubbing faults and are used for hybrid feature extraction. Experimental results demonstrated that the proposed approach effectively defines meaningful IMFs for rubbing processes, and the presented hybrid feature model allows for the classification of rub-impact faults of various intensities with good accuracy. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 5494 KiB

Open AccessArticle

DC-Link Voltage and Catenary Current Sensors Fault Reconstruction for Railway Traction Drives

by Fernando Garramiola, Javier Poza, Jon Del Olmo, Patxi Madina and Gaizka Almandoz

Sensors 2018, 18(7), 1998; https://doi.org/10.3390/s18071998 - 22 Jun 2018

Cited by 7 | Viewed by 3599

Due to the importance of sensors in control strategy and safety, early detection of faults in sensors has become a key point to improve the availability of railway traction drives. The presented sensor fault reconstruction is based on sliding mode observers and equivalent [...] Read more.

Due to the importance of sensors in control strategy and safety, early detection of faults in sensors has become a key point to improve the availability of railway traction drives. The presented sensor fault reconstruction is based on sliding mode observers and equivalent injection signals, and it allows detecting defective sensors and isolating faults. Moreover, the severity of faults is provided. The proposed on-board fault reconstruction has been validated in a hardware-in-the-loop platform, composed of a real-time simulator and a commercial traction control unit for a tram. Low computational resources, robustness to measurement noise, and easiness to tune are the main requirements for industrial acceptance. As railway applications are not safety-critical systems, compared to aerospace applications, a fault evaluation procedure is proposed, since there is enough time to perform diagnostic tasks. This procedure analyses the fault reconstruction in the steady state, delaying the decision-making in some seconds, but minimising false detections. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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23 pages, 5876 KiB

Open AccessArticle

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier

by Shenghan Zhou, Silin Qian, Wenbing Chang, Yiyong Xiao and Yang Cheng

Sensors 2018, 18(6), 1934; https://doi.org/10.3390/s18061934 - 14 Jun 2018

Cited by 83 | Viewed by 4966

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), [...] Read more.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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24 pages, 2023 KiB

Open AccessArticle

Proprioceptive Sensors’ Fault Tolerant Control Strategy for an Autonomous Vehicle

by Mohamed Riad Boukhari, Ahmed Chaibet, Moussa Boukhnifer and Sébastien Glaser

Sensors 2018, 18(6), 1893; https://doi.org/10.3390/s18061893 - 09 Jun 2018

Cited by 26 | Viewed by 5269

In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle’s speed sensor and then to keep the vehicle in a safe state. For [...] Read more.

In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle’s speed sensor and then to keep the vehicle in a safe state. For this purpose, the separation principle, composed of a static output feedback controller and fault estimation observers, is designed. Indeed, two observer techniques were proposed: the proportional and integral observer and the descriptor observer. The effectiveness of the proposed scheme is validated by means of the experimental demonstrator of the VEDECOM (Véhicle Décarboné et Communinicant) Institut. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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27 pages, 737 KiB

Open AccessArticle

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

by Funa Zhou, Ju H. Park, Chenglin Wen and Po Hu

Sensors 2018, 18(6), 1804; https://doi.org/10.3390/s18061804 - 03 Jun 2018

Cited by 10 | Viewed by 2747

Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method [...] Read more.

Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault size as well as decrease the noise energy. Then, designated component analysis (DCA) is introduced for developing an AA-DCA method to diagnose the root cause of the fault, which is helpful for the operator to make maintenance decisions. Combining the advantage of the cumulative sum (CUSUM) based method and the AA based method, a CUSUM-AA based method is developed to detect faults at earlier times. Finally, the remaining useful life (RUL) prediction model with error correction is established by nonlinear fitting. Once online fault size defined by detection statistics is obtained by an early diagnosis algorithm, real-time RUL prediction can be directly estimated without extra recursive regression. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 7103 KiB

Open AccessArticle

Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum

by Yong Li, Gang Cheng, Yusong Pang and Moshen Kuai

Sensors 2018, 18(6), 1735; https://doi.org/10.3390/s18061735 - 28 May 2018

Cited by 40 | Viewed by 4838

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction [...] Read more.

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 6734 KiB

Open AccessArticle

Spectral Kurtosis Entropy and Weighted SaE-ELM for Bogie Fault Diagnosis under Variable Conditions

by Zhipeng Wang, Limin Jia, Linlin Kou and Yong Qin

Sensors 2018, 18(6), 1705; https://doi.org/10.3390/s18061705 - 24 May 2018

Cited by 15 | Viewed by 3909

Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods [...] Read more.

Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods to deal with variable conditions. Besides, considering that the normal data for training are much more than the faulty data in practice, there is another problem in that only a small amount of data is available that includes faults. Concerning these issues, this paper proposes two new algorithms: (1) A novel feature parameter named spectral kurtosis entropy (SKE) is proposed based on the protrugram. The SKE not only avoids the manual post-processing of the protrugram but also has strong robustness to the operating conditions and parameter configurations, which have been validated by a simulation experiment in this paper. In this paper, the SKE, in conjunction with variational mode decomposition (VMD), is employed for feature extraction under variable conditions. (2) A new learning algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM) is proposed. WSaE-ELM gives each sample an extra sample weight to rebalance the training data and optimizes these weights along with the parameters of hidden neurons by means of the self-adaptive differential evolution algorithm. Finally, the hybrid method based on VMD, SKE, and WSaE-ELM is verified by using the vibration signals gathered from real bogies with speed variations. It is demonstrated that the proposed method of bogie fault diagnosis outperforms the conventional methods by up to 4.42% and 6.22%, respectively, in percentages of accuracy under variable conditions. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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20 pages, 15924 KiB

Open AccessArticle

Integral Sensor Fault Detection and Isolation for Railway Traction Drive

by Fernando Garramiola, Jon Del Olmo, Javier Poza, Patxi Madina and Gaizka Almandoz

Sensors 2018, 18(5), 1543; https://doi.org/10.3390/s18051543 - 13 May 2018

Cited by 28 | Viewed by 5720

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, [...] Read more.

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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20 pages, 8997 KiB

Open AccessArticle

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

by Chang Liu, Gang Cheng, Xihui Chen and Yusong Pang

Sensors 2018, 18(5), 1523; https://doi.org/10.3390/s18051523 - 11 May 2018

Cited by 88 | Viewed by 5946

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to [...] Read more.

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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13 pages, 2107 KiB

Open AccessArticle

Sensor Fault Detection and System Reconfiguration for DC-DC Boost Converter

by Jian Li, Zhigang Zhang and Bo Li

Sensors 2018, 18(5), 1375; https://doi.org/10.3390/s18051375 - 28 Apr 2018

Cited by 8 | Viewed by 3561

This paper presents a effective sensor fault detection and system reconfiguration approach for DC-DC Boost converter. We consider to design a Luenberger observer to solve the problem of the sensor fault detection of the DC-DC Boost converter. We establish mathematical model according to [...] Read more.

This paper presents a effective sensor fault detection and system reconfiguration approach for DC-DC Boost converter. We consider to design a Luenberger observer to solve the problem of the sensor fault detection of the DC-DC Boost converter. We establish mathematical model according to the state of the switch . Luenberger observer is designed to produce residual errors and analyse faults. We detect three types of sensor faults online and compare residuals with thresholds. The system is able to maintain stability by taking system reconfiguration approach. The effectiveness of this approach is demonstrated by simulations. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 2406 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

by Kun He, Zhijun Yang, Yun Bai, Jianyu Long and Chuan Li

Sensors 2018, 18(4), 1298; https://doi.org/10.3390/s18041298 - 23 Apr 2018

Cited by 37 | Viewed by 7513

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the [...] Read more.

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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17 pages, 3217 KiB

Open AccessArticle

Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis

by Muhammad Naveed Yasir and Bong-Hwan Koh

Sensors 2018, 18(4), 1278; https://doi.org/10.3390/s18041278 - 21 Apr 2018

Cited by 27 | Viewed by 4925

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate [...] Read more.

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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20 pages, 7835 KiB

Open AccessArticle

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

by Suliang Ma, Mingxuan Chen, Jianwen Wu, Yuhao Wang, Bowen Jia and Yuan Jiang

Sensors 2018, 18(4), 1221; https://doi.org/10.3390/s18041221 - 16 Apr 2018

Cited by 39 | Viewed by 4397

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and [...] Read more.

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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21 pages, 20255 KiB

Open AccessArticle

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

by Rui Yuan, Yong Lv and Gangbing Song

Sensors 2018, 18(4), 1210; https://doi.org/10.3390/s18041210 - 16 Apr 2018

Cited by 40 | Viewed by 4339

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the [...] Read more.

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 18450 KiB

Open AccessArticle

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

by Bach Phi Duong and Jong-Myon Kim

Sensors 2018, 18(4), 1129; https://doi.org/10.3390/s18041129 - 07 Apr 2018

Cited by 31 | Viewed by 4642

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for [...] Read more.

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 23648 KiB

Open AccessArticle

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer

by Farzin Piltan and Jong-Myon Kim

Sensors 2018, 18(4), 1128; https://doi.org/10.3390/s18041128 - 07 Apr 2018

Cited by 43 | Viewed by 4709

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties [...] Read more.

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 43042 KiB

Open AccessArticle

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

by Si-Yuan Wang, Ding-Xin Yang and Hai-Feng Hu

Sensors 2018, 18(4), 1111; https://doi.org/10.3390/s18041111 - 05 Apr 2018

Cited by 8 | Viewed by 5016

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been [...] Read more.

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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19 pages, 32301 KiB

Open AccessArticle

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

by Bangyue Ren, Yansong Hao, Huaqing Wang, Liuyang Song, Gang Tang and Hongfang Yuan

Sensors 2018, 18(4), 1003; https://doi.org/10.3390/s18041003 - 28 Mar 2018

Cited by 18 | Viewed by 3412

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm [...] Read more.

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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17 pages, 4789 KiB

Open AccessArticle

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

by Moshen Kuai, Gang Cheng, Yusong Pang and Yong Li

Sensors 2018, 18(3), 782; https://doi.org/10.3390/s18030782 - 05 Mar 2018

Cited by 88 | Viewed by 4823

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing [...] Read more.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 1133 KiB

Open AccessArticle

Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts

by Yunji Li and Li Peng

Sensors 2018, 18(3), 731; https://doi.org/10.3390/s18030731 - 28 Feb 2018

Cited by 10 | Viewed by 2855

Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator [...] Read more.

Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator through a series of relays over a multi-hop link. In this paper, we consider a network with sensor nodes and relay nodes where the relay nodes can forward the estimated values to the remote estimator. An event-triggered remote estimator of state and fault with the corresponding data-forwarding scheme is investigated for stochastic systems subject to both randomly occurring nonlinearity and randomly occurring packet dropouts governed by Bernoulli-distributed sequences to achieve a trade-off between estimation accuracy and energy consumption. Recursive Riccati-like matrix equations are established to calculate the estimator gain to minimize an upper bound of the estimator error covariance. Subsequently, a sufficient condition and data-forwarding scheme are presented under which the error covariance is mean-square bounded in the multi-hop links with random packet dropouts. Furthermore, implementation issues of the theoretical results are discussed where a new data-forwarding communication protocol is designed. Finally, the effectiveness of the proposed algorithms and communication protocol are extensively evaluated using an experimental platform that was established for performance evaluation with a sensor and two relay nodes. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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22 pages, 5313 KiB

Open AccessArticle

Ontology-Based Method for Fault Diagnosis of Loaders

by Feixiang Xu, Xinhui Liu, Wei Chen, Chen Zhou and Bingwei Cao

Sensors 2018, 18(3), 729; https://doi.org/10.3390/s18030729 - 28 Feb 2018

Cited by 41 | Viewed by 4907

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model [...] Read more.

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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21 pages, 7254 KiB

Open AccessArticle

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

by Cai Yi, Dong Wang, Wei Fan, Kwok-Leung Tsui and Jianhui Lin

Sensors 2018, 18(3), 704; https://doi.org/10.3390/s18030704 - 27 Feb 2018

Cited by 30 | Viewed by 5299

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a [...] Read more.

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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15 pages, 4664 KiB

Open AccessArticle

Auto-Calibration and Fault Detection and Isolation of Skewed Redundant Accelerometers in Measurement While Drilling Systems

by Seyed Mohsen Seyed Moosavi, Bijan Moaveni, Behzad Moshiri and Mohammad Reza Arvan

Sensors 2018, 18(3), 702; https://doi.org/10.3390/s18030702 - 27 Feb 2018

Cited by 10 | Viewed by 4246

The present study designed skewed redundant accelerometers for a Measurement While Drilling (MWD) tool and executed auto-calibration, fault diagnosis and isolation of accelerometers in this tool. The optimal structure includes four accelerometers was selected and designed precisely in accordance with the physical shape [...] Read more.

The present study designed skewed redundant accelerometers for a Measurement While Drilling (MWD) tool and executed auto-calibration, fault diagnosis and isolation of accelerometers in this tool. The optimal structure includes four accelerometers was selected and designed precisely in accordance with the physical shape of the existing MWD tool. A new four-accelerometer structure was designed, implemented and installed on the current system, replacing the conventional orthogonal structure. Auto-calibration operation of skewed redundant accelerometers and all combinations of three accelerometers have been done. Consequently, biases, scale factors, and misalignment factors of accelerometers have been successfully estimated. By defecting the sensors in the new optimal skewed redundant structure, the fault was detected using the proposed FDI method and the faulty sensor was diagnosed and isolated. The results indicate that the system can continue to operate with at least three correct sensors. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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17 pages, 1043 KiB

Open AccessArticle

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

by Nannan Zhang, Lifeng Wu, Jing Yang and Yong Guan

Sensors 2018, 18(2), 463; https://doi.org/10.3390/s18020463 - 05 Feb 2018

Cited by 72 | Viewed by 5981

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in [...] Read more.

The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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13 pages, 2103 KiB

Open AccessArticle

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network

by Quansheng Jiang, Yehu Shen, Hua Li and Fengyu Xu

Sensors 2018, 18(2), 337; https://doi.org/10.3390/s18020337 - 24 Jan 2018

Cited by 42 | Viewed by 4032

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic [...] Read more.

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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25 pages, 4121 KiB

Open AccessArticle

Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks

by Yunji Li, QingE Wu and Li Peng

Sensors 2018, 18(2), 321; https://doi.org/10.3390/s18020321 - 23 Jan 2018

Cited by 20 | Viewed by 3888

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is [...] Read more.

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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14 pages, 3549 KiB

Open AccessArticle

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

by Woonsang Baek, Sujeong Baek and Duck Young Kim

Sensors 2018, 18(1), 154; https://doi.org/10.3390/s18010154 - 08 Jan 2018

Cited by 5 | Viewed by 3589

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity [...] Read more.

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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18 pages, 11403 KiB

Open AccessArticle

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions

by Yue Hu, Xiaotong Tu, Fucai Li and Guang Meng

Sensors 2018, 18(1), 150; https://doi.org/10.3390/s18010150 - 07 Jan 2018

Cited by 36 | Viewed by 6261

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary [...] Read more.

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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8076 KiB

Open AccessArticle

Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

by Zhinong Jiang, Zhiwei Mao, Zijia Wang and Jinjie Zhang

Sensors 2017, 17(12), 2916; https://doi.org/10.3390/s17122916 - 15 Dec 2017

Cited by 24 | Viewed by 7037

Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals [...] Read more.

Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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2474 KiB

Open AccessArticle

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

by Muhammad Sohaib, Cheol-Hong Kim and Jong-Myon Kim

Sensors 2017, 17(12), 2876; https://doi.org/10.3390/s17122876 - 11 Dec 2017

Cited by 160 | Viewed by 9659

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is [...] Read more.

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs). Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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3789 KiB

Open AccessArticle

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

by Viet Tra, Jaeyoung Kim, Sheraz Ali Khan and Jong-Myon Kim

Sensors 2017, 17(12), 2834; https://doi.org/10.3390/s17122834 - 06 Dec 2017

Cited by 63 | Viewed by 7493

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as [...] Read more.

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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Review

Jump to: Research

35 pages, 3125 KiB

Open AccessReview

Monitoring System Analysis for Evaluating a Building’s Envelope Energy Performance through Estimation of Its Heat Loss Coefficient

by Catalina Giraldo-Soto, Aitor Erkoreka, Laurent Mora, Irati Uriarte and Luis Alfonso Del Portillo

Sensors 2018, 18(7), 2360; https://doi.org/10.3390/s18072360 - 20 Jul 2018

Cited by 15 | Viewed by 4674

The present article investigates the question of building energy monitoring systems used for data collection to estimate the Heat Loss Coefficient (HLC) with existing methods, in order to determine the Thermal Envelope Performance (TEP) of a building. The data requirements of HLC estimation [...] Read more.

The present article investigates the question of building energy monitoring systems used for data collection to estimate the Heat Loss Coefficient (HLC) with existing methods, in order to determine the Thermal Envelope Performance (TEP) of a building. The data requirements of HLC estimation methods are related to commonly used methods for fault detection, calibration, and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests to estimate the HLC undertaken since 1978, qualitative and quantitative analyses of the Monitoring and Controlling System (MCS) specifications have been carried out. The results show that no Fault Detection and Diagnosis (FDD) methods have been implemented in the reviewed literature. Furthermore, it was not possible to identify a trend of technology type used in sensors, hardware, software, and communication protocols, because a high percentage of the reviewed experimental tests do not specify the model, technical characteristics, or selection criteria of the implemented MCSs. Although most actual Building Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply HLC estimation methods. Full article

(This article belongs to the Special Issue Sensors for Fault Detection)

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