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Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 12748

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Dr. Francisco Vedreño-Santos

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Guest Editor

School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: diagnostics and condition monitoring of electric machines; signal processing and design and control of power electronics for wind power applications

Prof. Dr. Daniel Morinigo-Sotelo

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Guest Editor

Research Group HSPdigital-ADIRE, Institute of Advanced Production Technologies (ITAP), University of Valladolid, 47011 Valladolid, Spain
Interests: fault detection and diagnostics of induction machines; power quality; smart grids
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Prof. Dr. Lucia Frosini

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Guest Editor

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy
Interests: design, modelling, simulation, and diagnostics of rotating electrical machines (induction motors, permanent magnet synchronous motors, synchronous reluctance motors), as well as transformers, especially from the electromagnetic point of view
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of electrical machine fault diagnosis and motor drive control has witnessed unprecedented advancements in recent years, driven by the integration of emerging theories and applications. Electrical machine fault diagnosis and motor drive control play pivotal roles in ensuring the reliability, efficiency and safety of electrical machines and drives, making them indispensable components in industrial applications. This Special Issue aims to explore and showcase the latest developments in this dynamic and critical research area by exploring emerging theories and methodologies that push the boundaries of fault diagnosis and motor drive control, investigating the application of state-of-the-art technologies and new signal processing techniques to enhancing fault detection and motor drive performance and showcasing innovative control strategies that ensure the stability, efficiency, and adaptability of motor drive systems in the presence of faults.

The advent of advanced technologies and development in new signal processing techniques has significantly influenced the landscape of electrical machine fault diagnosis and motor drive control. These have empowered researchers and practitioners to design more robust and reliable systems capable of detecting and mitigating incipient faults, even in real time. Additionally, the escalating demand for energy-efficient and high-performance motor drive systems has spurred innovative approaches in control strategies, encompassing new predictive and adaptive methodologies.

The reliability and efficiency of motor drive systems are paramount in numerous industries, including manufacturing, transportation, and renewable energy. Faults in these systems can lead to catastrophic consequences, resulting in substantial economic losses and safety hazards. Hence, the development of advanced fault diagnosis techniques for electrical machines and new motor drive control strategies are crucial to pre-emptively identify and address these issues, ensuring continuous and safe operation.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Diagnosis of three-phase and multi-phase induction machines.
Diagnosis of three-phase and multi-phase synchronous machines.
Diagnosis of three-phase and multi-phase permanent magnet machines.
New signal processing techniques for the detection of electrical machine’s faults under transient and steady-state conditions.
New signal processing techniques for the detection of electrical machine faults fed from converters.
Fault-tolerant motor drives control strategies.

Dr. Francisco Vedreño-Santos
Prof. Dr. Daniel Morinigo-Sotelo
Prof. Dr. Lucia Frosini
Guest Editors

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Keywords

fault detection
electrical machines
induction machines
synchronous machines
permanent magnet motor drive
signal processing
multi-phase electrical machines
fault-tolerant drives
fault-tolerant control strategies
motor drive control

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Published Papers (11 papers)

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Research

27 pages, 970 KiB

Open AccessArticle

Automatic Classification of Rotating Rectifier Faults in Brushless Synchronous Machines

by Kumar Mahtani, Julien Decroix, Rubén Pascual, José M. Guerrero and Carlos A. Platero

Electronics 2024, 13(23), 4667; https://doi.org/10.3390/electronics13234667 (registering DOI) - 26 Nov 2024

Abstract

This paper presents an advanced automatic fault classification method for detecting rotating rectifier faults in brushless synchronous machines (BSMs). The proposed approach employs a multilayer perceptron (MLP) neural network to classify the operational states of the rotating rectifier, including healthy conditions and common fault types: open-diode (OD), shorted-diode (SD), and open-phase (OP). Key machine measurements, available on an ordinary basis in the industry, such as active power (P), reactive power (Q), stator voltage (U), and excitation current (I_e), are used as inputs for this model, allowing for non-invasive, real-time fault detection. This model achieved an overall classification accuracy of 93.4%, with a precision of 94.9% for fault detection and strong recall performance across multiple fault types. The neural network’s robustness is enhanced by advanced data processing techniques, including Gaussian filtering and class balancing through the synthetic minority over-sampling technique (SMOTE). Experimental testing on a modified 5-kVA BSM setup, where rectifier faults were systematically induced, was used to train the network and validate the model’s performance. This method provides a promising tool for real-time condition monitoring of BSMs, improving machine reliability and minimizing downtime in industrial applications. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

17 pages, 4866 KiB

Open AccessArticle

Start-Up and Steady-State Regimes Automatic Separation in Induction Motors by Means of Short-Time Statistics

by Jonathan Cureño-Osornio, Carlos A. Alvarez-Ugalde, Israel Zamudio-Ramirez, Roque A. Osornio-Rios, Larisa Dunai, Dinu Turcanu and Jose A. Antonino-Daviu

Electronics 2024, 13(19), 3850; https://doi.org/10.3390/electronics13193850 - 28 Sep 2024

Viewed by 805

Abstract

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD). Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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22 pages, 11405 KiB

Open AccessArticle

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

by Abderrahman El Idrissi, Aziz Derouich, Said Mahfoud, Najib El Ouanjli, Hamid Chojaa and Ahmed Chantoufi

Electronics 2024, 13(16), 3195; https://doi.org/10.3390/electronics13163195 - 13 Aug 2024

Cited by 1 | Viewed by 980

Abstract

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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27 pages, 24406 KiB

Open AccessFeature PaperArticle

Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings

by Przemyslaw Pietrzak, Marcin Wolkiewicz and Jan Kotarski

Electronics 2024, 13(15), 2975; https://doi.org/10.3390/electronics13152975 - 28 Jul 2024

Viewed by 818

Abstract

Permanent-magnet synchronous motors (PMSMs) have played a key role in recent years in both industrial and commercial applications. Despite their many significant advantages, such as high efficiency, very good dynamics, and high power density, these types of motors are prone to various types of faults. This article proposes a low-cost microcontroller-based system for PMSM stator winding condition monitoring and fault diagnosis. It meets the demand created by the use of more and more low-budget solutions in industrial and commercial applications. A printed circuit board (PCB) has been developed to measure PMSM stator phase currents, which are used as diagnostic signals. The key components of this PCB are LEM’s LESR 6-NP current transducers. The acquisition and processing of diagnostic signals using a low-cost embedded system (NUCLEO-H7A3ZI-Q) with an ARM Cortex-M core is described in detail. A machine learning-driven KNN-based fault diagnostic algorithm is implemented to detect and classify incipient PMSM stator winding faults (interturn short-circuits). The effects of the severity of the fault and the motor operating conditions on the symptom extraction process are also investigated. The results of experimental tests conducted on a 2.5 kW PMSM confirmed the effectiveness of the developed system. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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21 pages, 8752 KiB

Open AccessArticle

Data-Driven Rotary Machine Fault Diagnosis Using Multisensor Vibration Data with Bandpass Filtering and Convolutional Neural Network for Signal-to-Image Recognition

by Dominik Łuczak

Electronics 2024, 13(15), 2940; https://doi.org/10.3390/electronics13152940 - 25 Jul 2024

Viewed by 847

Abstract

This paper proposes a novel data-driven method for machine fault diagnosis, named multisensor-BPF-Signal2Image-CNN2D. This method uses multisensor data, bandpass filtering (BPF), and a 2D convolutional neural network (CNN2D) for signal-to-image recognition. The proposed method is particularly suitable for scenarios where traditional time-domain analysis might be insufficient due to the complexity or similarity of the data. The results demonstrate that the multisensor-BPF-Signal2Image-CNN2D method achieves high accuracy in fault classification across the three datasets (constant-velocity fan imbalance, variable-velocity fan imbalance, Case Western Reserve University Bearing Data Center). In particular, the proposed multisensor method exhibits a significantly faster training speed compared to the reference IMU6DoF-Time2GrayscaleGrid-CNN, IMU6DoF-Time2RGBbyType-CNN, and IMU6DoF-Time2RGBbyAxis-CNN methods, which use the signal-to-image approach, requiring fewer iterations to achieve the desired level of accuracy. The interpretability of the model is also explored. This research demonstrates the potential of bandpass filters in the signal-to-image approach with a CNN2D to be robust and interpretable in selected frequency bandwidth machine fault diagnosis using multiple sensor data. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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17 pages, 6197 KiB

Open AccessArticle

Applications of the TL-Based Fault Diagnostic System for the Capacitor in Hybrid Aircraft

by Maciej Skowron, Stanisław Oliszewski, Mateusz Dybkowski, Jeremi Jan Jarosz, Marcin Pawlak, Sebastien Weisse, Jerome Valire, Agnieszka Wyłomańska, Radosław Zimroz and Krzysztof Szabat

Electronics 2024, 13(9), 1638; https://doi.org/10.3390/electronics13091638 - 24 Apr 2024

Viewed by 1463

Abstract

The article concerns the problem of capacitor diagnosis in a hybrid aircraft. Capacitors are one of the most commonly damaged components of electrical vehicle drive systems. The result of these failures is an increase in voltage ripple. Most known analytical methods are based on frequency spectrum analysis, which is time-consuming and computationally complex. The use of deep neural networks (DNNs) allows for the direct use of the measurement signal, which reduces the operating time of the overall diagnostic system. However, the problem with these networks is the long training process. Therefore, this article uses transfer learning (TL), which allows for the secondary use of previously learnt DNNs. To collect data to learn the network, a test bench with the ability to simulate a capacitor failure was constructed, and a model based on it was made in the MATLAB/Simulink environment. A convolutional neural network (CNN) structure was developed and trained by the TL method to estimate the capacitance of the capacitor based on signals from the Simulink-designed model. The proposed fault diagnostic method is characterised by a nearly 100% efficiency in determining capacitance, with an operating time of about 10 ms, regardless of load and supply voltage. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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28 pages, 5555 KiB

Open AccessArticle

Evaluation of Entropy Analysis as a Fault-Related Feature for Detecting Faults in Induction Motors and Their Kinematic Chain

by Arturo Y. Jaen-Cuellar, Juan J. Saucedo-Dorantes, David A. Elvira-Ortiz and Rene de J. Romero-Troncoso

Electronics 2024, 13(8), 1524; https://doi.org/10.3390/electronics13081524 - 17 Apr 2024

Viewed by 807

Abstract

The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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20 pages, 6800 KiB

Open AccessArticle

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM

by Muhammad Amir Khan, Bilal Asad, Toomas Vaimann and Ants Kallaste

Electronics 2024, 13(7), 1292; https://doi.org/10.3390/electronics13071292 - 30 Mar 2024

Cited by 2 | Viewed by 1553

Abstract

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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18 pages, 3368 KiB

Open AccessArticle

Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

by Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio and Jang-Wook Hur

Electronics 2024, 13(5), 926; https://doi.org/10.3390/electronics13050926 - 29 Feb 2024

Cited by 2 | Viewed by 1494

Abstract

The role of transformers in power distribution is crucial, as their reliable operation is essential for maintaining the electrical grid’s stability. Single-phase transformers are highly versatile, making them suitable for various applications requiring precise voltage control and isolation. In this study, we investigated the fault diagnosis of a 1 kVA single-phase transformer core subjected to induced faults. Our diagnostic approach involved using a combination of advanced signal processing techniques, such as the fast Fourier transform (FFT) and Hilbert transform (HT), to analyze the current signals. Our analysis aimed to differentiate and characterize the unique signatures associated with each fault type, utilizing statistical feature selection based on the Pearson correlation and a machine learning classifier. Our results showed significant improvements in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, with 83.89% accuracy and a computational cost of 0.2963 s. For future studies, our focus will be on using deep learning models to improve the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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19 pages, 14858 KiB

Open AccessArticle

Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection

by Pawel Ewert, Bartłomiej Wicher and Tomasz Pajchrowski

Electronics 2024, 13(2), 441; https://doi.org/10.3390/electronics13020441 - 21 Jan 2024

Cited by 3 | Viewed by 1320

Abstract

The article focuses on the use of short-time Fourier transform (STFT) to detect the unbalance of a drive with a flexible connection between the driving machine and the load. The authors present the unbalance model and justify, through subsequent experiments, why the STFT-based approach is appropriate. The effectiveness of the presented method of analyzing signals from acceleration sensors was confirmed experimentally by designing an artificial neural network for detecting the unbalance. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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19 pages, 17855 KiB

Open AccessArticle

Comprehensive Diagnosis of Localized Rolling Bearing Faults during Rotating Machine Start-Up via Vibration Envelope Analysis

by Jose E. Ruiz-Sarrio, Jose A. Antonino-Daviu and Claudia Martis

Electronics 2024, 13(2), 375; https://doi.org/10.3390/electronics13020375 - 16 Jan 2024

Cited by 7 | Viewed by 1696

Abstract

The analysis of electrical machine faults during start-up, and variable speed and load conditions offers numerous advantages for fault detection and diagnosis. In this context, diagnosing localized bearing faults through vibration signals remains challenging, particularly in developing physically meaningful, simple, and resampling-free techniques to monitor fault characteristic components throughout machine start-up. This study introduces a straightforward method for qualitatively identifying the time-frequency evolutions of localized bearing faults during the start-up of an inverter-fed machine. The proposed technique utilizes the time-frequency representation of the envelope spectrum, effectively highlighting characteristic fault frequencies during transient operation. The method is tested in an open-source dataset including transient vibration signals. In addition, the work studies the method limitations induced by the mechanical transfer path, when the bearing surroundings are not directly accessible for vibration acquisition. The proposed methodology efficiently identifies incipient localized bearing faults during inverter-fed machine start-up when the fault signature is not highly attenuated. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Fault Diagnosis and Motor Drive Control)

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