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Sensors for Fault Diagnosis and Fault Tolerance

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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 (20 February 2019) | Viewed by 59141

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Special Issue Editors

Prof. Dr. Manuel Pineda-Sanchez

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Guest Editor

Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: fault diagnosis of electrical machines; reduced order-modeling of electromagnetic devices; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Martin Riera-Guasp

E-Mail Website
Guest Editor

Institute for Energy Engineering, Universitat Politècnica de València, Valencia, Spain
Interests: condition monitoring of electrical machines; applications of signal analysis techniques to electrical engineering and efficiency in electric power applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ruben Puche-Panadero

E-Mail Website
Guest Editor

Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: induction motor fault diagnosis; numerical modeling of electrical machines; advanced automation processes and electrical installations
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Javier Martinez-Roman

E-Mail Website
Guest Editor

Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: fault diagnosis of electrical machines; control of electrical machines and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Automation of industrial processes (smart factories, collaborative robots, Industry 4.0) and everyday life (smart buildings, automated driving) play an increasing role in the advancement of modern societies. Due to the complexity of automated installations, the prevention of safety hazards and huge production losses require the detection and identification of any kind of fault, as early as possible, and to minimize their impacts by implementing real-time fault detection (FD) and fault-tolerant (FT) operations systems. Therefore, FD and FT technologies have attracted an increasing amount of research and industrial attention in recent years. Modern FD and FT schemas demand high-volume, high-quality information from multiple types of sensor data, but sensors are also subject to failure, which must be included in the diagnostic systems. The integration of distributed sensor networks in model-based, signal-based, knowledge-based, and hybrid/active diagnostic systems is a challenging issue, which requires techniques from a broad set of disciplines, such as artificial intelligence, adaptive observers design, statistical estimation, data dimension reduction techniques, etc. Robust active and passive FT approaches that can tolerate discontinuities and errors in the flow of sensors data are needed to increase the reliability of automated systems. A particular, a growing trend in recent years has been the use of electric machines and electronic drives as a source of signals for FD/FT industrial systems, acting as sensors.

We invite academia and industry researchers to submit original and unpublished manuscripts to this Special Issue that develop research works related to these topics.

The goal of the Special Issue is to publish the most recent research results and industrial applications of sensors in FD and FT methods. Topics suited for this Special Issue include, but are not limited to:

Data-driven and model-based sensor fault diagnosis.
Self-healing, self-organizing, self-adaptive, automatic recovery of sensor networks.
Integration of high volume sensor data in the design of fault diagnosis and fault-tolerant control.
Sensors in advanced FD/FT applications in different industrial sectors.
Methods, concepts and performance assessment for improving FD/FT existing in industrial applications.
Electrical drives as sensors in industrial processes.
Sensors systems for fault tolerant electrical drives.

Prof. Dr. Manuel Pineda-Sanchez
Prof. Dr. Martin Riera-Guasp
Prof. Dr. Ruben Puche-Panadero
Prof. Dr. Javier Martinez-Roman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Distribured sensors networks
Data-driven fault diagnosis systems
Active fault tolerant process control
Fault tolerant sensors networks
Knowledge based fault diagnosis and fault tolerant control systems
Electrical machines and drives as sensors for fault diagnosis

Related Special Issue

Sensors for Fault Diagnosis and Fault Tolerance Ⅱ in Sensors (2 articles)

Published Papers (13 papers)

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Research

18 pages, 6837 KiB

Open AccessArticle

Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network

by Hongmei Li, Jinying Huang and Shuwei Ji

Sensors 2019, 19(9), 2034; https://doi.org/10.3390/s19092034 - 30 Apr 2019

Cited by 88 | Viewed by 5641

Abstract

Rolling bearings are the core components of rotating machinery. Their health directly affects the performance, stability and life of rotating machinery. To prevent possible damage, it is necessary to detect the condition of rolling bearings for fault diagnosis. With the rapid development of intelligent fault diagnosis technology, various deep learning methods have been applied in fault diagnosis in recent years. Convolution neural networks (CNN) have shown high performance in feature extraction. However, the pooling operation of CNN can lead to the loss of much valuable information and the relationship between the whole and the part may be ignored. In this study, we proposed CNNEPDNN, a novel bearing fault diagnosis model based on ensemble deep neural network (DNN) and CNN. We firstly trained CNNEPDNN model. Each of its local networks was trained with different training datasets. The CNN used vibration sensor signals as the input, whereas the DNN used nine time-domain statistical features from bearing vibration sensor signals as the input. Each local network of CNNEPDNN extracted different features from its own trained dataset, thus we fused features with different discrimination for fault recognition. CNNEPDNN was tested under 10 fault conditions based on the bearing data from Bearing Data Center of Case Western Reserve University (CWRU). To evaluate the proposed model, four aspects were analyzed: convergence speed of training loss function, test accuracy, F-Score and the feature clustering result by t-distributed stochastic neighbor embedding (t-SNE) visualization. The training loss function of the proposed model converged more quickly than the local models under different loads. The test accuracy of the proposed model is better than that of CNN, DNN and BPNN. The F-Score value of the model is higher than that of CNN model, and the feature clustering effect of the proposed model was better than that of CNN. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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20 pages, 8586 KiB

Open AccessArticle

A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

by Jiung Huh, Huan Pham Van, Soonyoung Han, Hae-Jin Choi and Seung-Kyum Choi

Sensors 2019, 19(5), 1055; https://doi.org/10.3390/s19051055 - 01 Mar 2019

Cited by 9 | Viewed by 4097

Abstract

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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18 pages, 9084 KiB

Open AccessArticle

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network

by Zhaoyi Guan, Zhiqiang Liao, Ke Li and Peng Chen

Sensors 2019, 19(3), 591; https://doi.org/10.3390/s19030591 - 30 Jan 2019

Cited by 32 | Viewed by 3712

Abstract

To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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23 pages, 11418 KiB

Open AccessArticle

Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis

by Qi Chen, Wenfeng Tian, Wuwei Chen, Qadeer Ahmed and Yanming Wu

Sensors 2018, 18(12), 4468; https://doi.org/10.3390/s18124468 - 17 Dec 2018

Cited by 9 | Viewed by 7014

Abstract

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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20 pages, 4349 KiB

Open AccessArticle

Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

by Yunzhao Jia, Minqiang Xu and Rixin Wang

Sensors 2018, 18(12), 4460; https://doi.org/10.3390/s18124460 - 17 Dec 2018

Cited by 20 | Viewed by 3091

Abstract

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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16 pages, 5105 KiB

Open AccessArticle

Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods

by Manjurul Islam, Muhammad Sohaib, Jaeyoung Kim and Jong-Myon Kim

Sensors 2018, 18(12), 4379; https://doi.org/10.3390/s18124379 - 11 Dec 2018

Cited by 24 | Viewed by 7348

Abstract

Pressure vessels (PV) are designed to hold liquids, gases, or vapors at high pressures in various industries, but a ruptured pressure vessel can be incredibly dangerous if cracks are not detected in the early stage. This paper proposes a robust crack identification technique for pressure vessels using genetic algorithm (GA)-based feature selection and a deep neural network (DNN) in an acoustic emission (AE) examination. First, hybrid features are extracted from multiple AE sensors that represent diverse symptoms of pressure vessel faults. These features stem from various signal processing domains, such as the time domain, frequency domain, and time-frequency domain. Heterogenous features from various channels ensure a robust feature extraction process but are high-dimensional, so may contain irrelevant and redundant features. This can cause a degraded classification performance. Therefore, we use GA with a new objective function to select the most discriminant features that are highly effective for the DNN classifier when identifying crack types. The potency of the proposed method (GA + DNN) is demonstrated using AE data obtained from a self-designed pressure vessel. The experimental results indicate that the proposed method is highly effective at selecting discriminant features. These features are used as the input of the DNN classifier, achieving a 94.67% classification accuracy. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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17 pages, 6258 KiB

Open AccessArticle

Bearing Fault Diagnosis Using an Extended Variable Structure Feedback Linearization Observer

by Farzin Piltan and Jong-Myon Kim

Sensors 2018, 18(12), 4359; https://doi.org/10.3390/s18124359 - 10 Dec 2018

Cited by 27 | Viewed by 3098

Abstract

The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). The traditional feedback linearization observer is stable; however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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24 pages, 8875 KiB

Open AccessArticle

Design and Evaluation of a Structural Analysis-Based Fault Detection and Identification Scheme for a Hydraulic Torque Converter

by Qi Chen, Jincheng Wang and Qadeer Ahmed

Sensors 2018, 18(12), 4103; https://doi.org/10.3390/s18124103 - 23 Nov 2018

Cited by 5 | Viewed by 3462

Abstract

A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, are analyzed, and as a result, two speed sensors and two torque sensors of pump and turbine are selected to realize the maximal fault detectability and fault isolability: all six faults are detectable, four faults are uniquely isolable, and two faults are isolated from the other faults, but not from each other. Then five minimal structurally overdetermined (MSO) sets are easily acquired by SA to generate five corresponding residuals. The proposed FDI scheme of the HTC by SA is first validated by a theoretical model, then by an offline experiment in a commercial SUV, and the testing results indicate a consistent conclusion with the simulations and theory analysis. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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19 pages, 2457 KiB

Open AccessArticle

Motion Periods of Planet Gear Fault Meshing Behavior

by Mian Zhang, Kesheng Wang and Yaxin Li

Sensors 2018, 18(11), 3802; https://doi.org/10.3390/s18113802 - 06 Nov 2018

Cited by 16 | Viewed by 3778

Abstract

Vibration sensors are, generally, fixed on the housing of planetary gearboxes for vibration monitoring. When a local fault occurred on the tooth of a planet gear, along with the system operating, the faulty tooth will mesh with the ring gear or sun gear at different positions referring to the fixed sensor. With consideration of the attenuation effect, the amplitudes of the fault-induced vibrations will be time-varying due to the time-varying transfer paths. These variations in signals are valuable information to identify the fault existence as well as the severity and types. However, the fault-meshing positions are time-varying and elusive due to the complicated kinematics or the compound motion behaviors of the internal rotating components. It is tough to accurately determine every fault meshing position though acquiring information from multi-sensors. However, there should exist some specific patterns of the fault meshing positions referring to the single sensor. To thoroughly investigate these motion patterns make effective fault diagnosis feasible merely by a single sensor. Unfortunately, so far few pieces of literature explicitly demonstrate these motion patterns in this regard. This article proposes a method to derive the motion periods of the fault-meshing positions with a faulty planet gear tooth, in which two conditions are considered: 1. The fault-meshing position initially occurs at the ring gear; 2. The fault-meshing position initially occurs at the sun gear. For each scenario, we derive the mathematical expression of the motion period in terms of rotational angles. These motion periods are, in essence, based on the teeth number of gears of a given planetary gearbox. Finally, the application of these motion periods for fault diagnosis is explored with experimental studies. The minimal required data length of a single sensor for effective fault diagnosis is revealed based on the motion periods. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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16 pages, 2358 KiB

Open AccessArticle

A Reliable Health Indicator for Fault Prognosis of Bearings

by Bach Phi Duong, Sheraz Ali Khan, Dongkoo Shon, Kichang Im, Jeongho Park, Dong-Sun Lim, Byungtae Jang and Jong-Myon Kim

Sensors 2018, 18(11), 3740; https://doi.org/10.3390/s18113740 - 02 Nov 2018

Cited by 39 | Viewed by 5084

Abstract

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing’s HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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20 pages, 812 KiB

Open AccessArticle

A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning

by Dapeng Zhang, Zhiling Lin and Zhiwei Gao

Sensors 2018, 18(9), 3087; https://doi.org/10.3390/s18093087 - 13 Sep 2018

Cited by 16 | Viewed by 3358

Abstract

In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-signal ratio of the data series is minimized to achieve robustness. Based on the information of fault free data series, fault detection is promptly implemented by comparing with the model forecast and real-time process. The fault severity degrees are also discussed by measuring the distance between the healthy parameters and faulty parameters. The effectiveness of the algorithm is demonstrated by an example of a DC-motor system. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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12 pages, 5651 KiB

Open AccessArticle

Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis

by Junchao Guo, Zhanqun Shi, Haiyang Li, Dong Zhen, Fengshou Gu and Andrew D. Ball

Sensors 2018, 18(9), 2908; https://doi.org/10.3390/s18092908 - 01 Sep 2018

Cited by 30 | Viewed by 3908

Abstract

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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16 pages, 5557 KiB

Open AccessArticle

A Fiber Bragg Grating Based Torsional Vibration Sensor for Rotating Machinery

by Jingjing Wang, Li Wei, Ruiya Li, Qin Liu and Lingling Yu

Sensors 2018, 18(8), 2669; https://doi.org/10.3390/s18082669 - 14 Aug 2018

Cited by 11 | Viewed by 4179

Abstract

This paper proposes a new type of torsional vibration sensor based on fiber Bragg grating (FBG). The sensor has two mass ball optical fiber systems. The optical fiber is directly treated as an elastomer and a mass ball is fixed in the middle of the fiber in each mass ball fiber system, which is advantageously small, lightweight, and has anti-electromagnetic interference properties. The torsional vibration signal can be calculated by the four FBGs’ wavelength shifts, which are caused by mass balls. The difference in the two sets of mass ball optical fiber systems achieves anti-horizontal vibration and anti-temperature interference. The principle and model of the sensor, as well as numerical analysis and structural parameter design, are introduced. The experimental conclusions show that the minimum torsional natural frequency of the sensor is 27.35 Hz and the torsional vibration measurement sensitivity is 0.3603 pm/(rad/s²). Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)

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