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Sensors for Electric Machines Fault Diagnosis and Condition Monitoring

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 29241

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Special Issue Editors

Prof. Dr. Ruben Puche-Panadero

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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
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Prof. Dr. Javier Martinez-Roman

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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
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Prof. Dr. Angel Sapena-Bano

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Guest Editor

Prof. Dr. Jordi Burriel-Valencia

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Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: fault diagnosis; induction machines; induction motors
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Prof. Dr. van Khang Huynh

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Guest Editor

Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway
Interests: electric machine design and control; electromagnetic characterization; renewable energy
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Special Issue Information

Dear Colleagues,

Electrical machines are key components in industrial and domestic applications. Nowadays, their vital role has increased due to the rising interest in electrical mobility, renewable energy, robots, unmanned aerial vehicles, etc. Therefore, condition monitoring and fault diagnosis is a crucial matter for these applications in order to prevent or minimize the impact of sudden failures.

The integration of condition monitoring systems in control and information systems, SCADA sytems and even Cloud Services must improve the reliability and the efficiency of the processes.

Additionally, in recent years, the integration of sensors monitoring different magnitudes to develop fault diagnosis and monitoring technologies of electrical machines has attracted increasing attention from both academia and industry.

The effective integration of distributed sensor networks in diagnostic systems is a challenging issue which requires expertise from a broad set of disciplines, such as artificial intelligence, adaptive observer design, statistical estimation, data dimension reduction techniques, etc. On the other hand, the acquired information can be stored, processed, and delivered using modern cloud-based software services and big-data technologies.

We invite researchers from both academia and industry to submit original and unpublished manuscripts to this Special Issue to showcase some of the recent developments within these topics.

The goal of the Special Issue is to publish the most recent research results and industrial applications in sensors for electric machine fault diagnosis and condition monitoring. Topics that are suitable for this Special Issue include, but are not limited to:

Data-driven and model-based sensor fault diagnosis;
Integration of high-volume sensor data in the design of applications for condition monitoring of electrical machines and drives;
Sensors in advanced electrical machines—fault diagnosis and monitoring applications in different industrial sectors;
Methods, concepts, and performance assessment for improving the fault diagnosis of existing techniques in the field of electrical machines;
Electrical drives as sensors in industrial processes;
Cloud-based software services for fault diagnosis and monitoring of electrical machines.

Prof. Dr. Ruben Puche-Panadero
Prof. Dr. Javier Martinez-Roman
Prof. Dr. Angel Sapena-Bano
Prof. Dr. Jordi Burriel-Valencia
Prof. Dr. van Khang Huynh
Guest Editors

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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.

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Published Papers (11 papers)

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Research

20 pages, 3932 KiB

Open AccessArticle

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Ángel Sapena-Bañó and Javier Martínez-Román

Sensors 2023, 23(19), 8196; https://doi.org/10.3390/s23198196 - 30 Sep 2023

Cited by 11 | Viewed by 2120

Abstract

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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14 pages, 5007 KiB

Open AccessArticle

Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition

by Attiq Ur Rehman, Weidong Jiao, Jianfeng Sun, Muhammad Sohaib, Yonghua Jiang, Mahnoor Shahzadi and Muhammad Ijaz Khan

Sensors 2023, 23(16), 7109; https://doi.org/10.3390/s23167109 - 11 Aug 2023

Cited by 5 | Viewed by 1648

Abstract

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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28 pages, 14125 KiB

Open AccessArticle

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

by José Miguel Monzón-Verona, Pablo González-Domínguez and Santiago García-Alonso

Sensors 2023, 23(3), 1685; https://doi.org/10.3390/s23031685 - 3 Feb 2023

Cited by 2 | Viewed by 2301

Abstract

In this paper, an experimental analysis of the quality of electrical insulating oils is performed using a combination of dielectric loss and capacitance measurement tests. The transformer oil corresponds to a fresh oil sample. The paper follows the ASTM D 924-15 standard (standard test method for dissipation factor and relative permittivity of electrical insulating liquids). Effective electrical parameters, including the tan δ of the oil, were obtained in this non-destructive test. Subsequently, a numerical method is proposed to accurately determine the effective electrical resistivity, σ, and effective electrical permittivity, ε, of an insulating mineral oil from the data obtained in the experimental analysis. These two parameters are not obtained in the ASTM standard. We used the cell method and the multi-objective non-dominated sorting in genetic algorithm II (NSGA-II) for this purpose. In this paper, a new numerical tool to accurately obtain the effective electrical parameters of transformer insulating oils is therefore provided for fault detection and diagnosis. The results show improved accuracy compared to the existing analytical equations. In addition, as the experimental data are collected in a high-voltage domain, wireless sensors are used to measure, transmit, and monitor the electrical and thermal quantities. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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26 pages, 14072 KiB

Open AccessArticle

Analytical Modeling and Analysis of Permanent-Magnet Motor with Demagnetization Fault

by Cenwei Shi, Lin Peng, Zhen Zhang and Tingna Shi

Sensors 2022, 22(23), 9440; https://doi.org/10.3390/s22239440 - 2 Dec 2022

Cited by 5 | Viewed by 2548

Abstract

Factors such as insufficient heat dissipation and excessively high temperature can easily lead to demagnetization of the PMs in permanent-magnet (PM) motors. As a result, the magnetic field distribution of the motor will not be uniform, producing fault harmonics and lowering the operational performance of the motor. An essential stage in the diagnosis of faults and the monitoring of motor condition is the establishment of an accurate model of motors with demagnetization faults. In this paper, demagnetization faults are modeled by changing the Fourier coefficients in the Fourier expansion of the magnetization of PMs. This model can be used to determine the motor performance under various types of demagnetization, including radial air gap flux density, back electromotive force (EMF), and torque. On this basis, the corresponding relationship between the demagnetization degree and the fault signature is established, to provide a theoretical foundation for the subsequent demagnetization fault diagnosis. The finite element analysis (FEA) verifies the effectiveness and superiority of the proposed analytical model. The modeling method proposed in this paper can be applied to PM motors with PMs having different magnetization directions and shapes because it is based on the demagnetization region of PMs. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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23 pages, 5661 KiB

Open AccessArticle

Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

by Qiang Song, Mingsheng Wang, Wuxuan Lai and Sifang Zhao

Sensors 2022, 22(18), 6870; https://doi.org/10.3390/s22186870 - 11 Sep 2022

Cited by 11 | Viewed by 2373

Abstract

The diagnosis of an inter-turn short circuit (ITSC) fault at its early stage is very important in permanent magnet synchronous motors as these faults can lead to disastrous results. In this paper, a multiscale kernel-based residual convolutional neural network (CNN) algorithm is proposed for the diagnosis of ITSC faults. The contributions are majorly located on two sides. Firstly, a residual learning connection is embedded into a dilated CNN to overcome the defects of the conventional convolution and the degradation problem of a deep network. Secondly, a multiscale kernel algorithm is added to a residual dilated CNN architecture to extract high-dimension features from the collected current signals under complex operating conditions and electromagnetic interference. A motor fault experiment with both constant operating conditions and dynamics was conducted by setting the fault severity of the ITSC fault to 17 levels. Comparison with five other algorithms demonstrated the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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22 pages, 5694 KiB

Open AccessArticle

Application of Teager–Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings

by Xiangfu Shi, Zhen Zhang, Zhiling Xia, Binhua Li, Xin Gu and Tingna Shi

Sensors 2022, 22(17), 6673; https://doi.org/10.3390/s22176673 - 3 Sep 2022

Cited by 5 | Viewed by 2225

Abstract

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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22 pages, 5210 KiB

Open AccessArticle

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis

by Saeed Hasan Ebrahimi, Martin Choux and Van Khang Huynh

Sensors 2022, 22(9), 3407; https://doi.org/10.3390/s22093407 - 29 Apr 2022

Cited by 11 | Viewed by 2684

Abstract

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM [...] Read more.

a b c

frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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15 pages, 1231 KiB

Open AccessArticle

Transient Detection of Rotor Asymmetries in Squirrel-Cage Induction Motors Using a Model-Based Tacholess Order Tracking

by Erik Etien, Thierry Doget, Laurent Rambault, Sebastien Cauet, Anas Sakout and Sandrine Moreau

Sensors 2022, 22(9), 3371; https://doi.org/10.3390/s22093371 - 28 Apr 2022

Cited by 3 | Viewed by 2036

Abstract

In this article, we propose to determine the dynamic model of a squirrel-cage induction motor from a reduced amount of information. An adaptive observer is also built from this model in order to obtain a speed estimation and to perform rotor fault monitoring by Tacholess Order Tracking (TOT). We also propose a generalization of the notion of angular sampling in order to adapt to this type of defect. The procedure is validated in the laboratory on a test bench dedicated to the study of rotor bar defects. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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17 pages, 4050 KiB

Open AccessArticle

Calculation of Capacitive-Based Sensors of Rotating Shaft Vibration for Fault Diagnostic Systems of Powerful Generators

by Ievgen Zaitsev, Victoriia Bereznychenko, Mohit Bajaj, Ibrahim B. M. Taha, Youcef Belkhier, Vladyslav Titko and Salah Kamel

Sensors 2022, 22(4), 1634; https://doi.org/10.3390/s22041634 - 19 Feb 2022

Cited by 15 | Viewed by 3101

Abstract

This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the key to increasing the reliability of powerful turbines and hydro generators. The application of sensors in monitoring systems was considered, and the requirements for the sensors used were analyzed. Structures of concentric capacitive-based sensors of rotating shaft vibration based on the measurement of the capacitance value from the distance to the metal surface were proposed. The design scheme was created for determining electrode dimensions of the rotating shaft vibration capacitive-based sensors with concentric electrodes, and analytical dependences were obtained. The calculation results allow the selection of optimal parameters of the active and guard electrodes. Analytical and computer simulation methods determined the response functions of the capacitive sensors. Analytical calculations and simulation results using 3D FEM were used to find the response functions of the sensors. The calculation of the characteristics of the capacitive-based sensors of rotating shaft vibration is presented. The study of the influence of fringe effects was carried out using the obtained results of the modeling and analytical calculations. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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19 pages, 17373 KiB

Open AccessArticle

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

by Sveinung Attestog, Jagath Sri Lal Senanayaka, Huynh Van Khang and Kjell G. Robbersmyr

Sensors 2022, 22(3), 1216; https://doi.org/10.3390/s22031216 - 5 Feb 2022

Cited by 7 | Viewed by 2727

Abstract

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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22 pages, 4943 KiB

Open AccessArticle

Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions

by Mohamed Esam El-Dine Atta, Doaa Khalil Ibrahim, Mahmoud Gilany and Ahmed F. Zobaa

Sensors 2022, 22(1), 365; https://doi.org/10.3390/s22010365 - 4 Jan 2022

Cited by 4 | Viewed by 2492

Abstract

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters. Full article

(This article belongs to the Special Issue Sensors for Electric Machines Fault Diagnosis and Condition Monitoring)

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