An Intelligent Diagnostic Method for Permanent Magnet Synchronous Motors (PMSM) in the Electric Drive of Autonomous Vehicles

Shen, Tunan; Kiliç, Ahmet; Thulfaut, Christian; Reuss, Hans-Christian

doi:10.23919/epe.2019.8915161

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…The analysis of the literature presented in Table VIII shows that the largest number of works related to the design of neural damage detectors of PMSMs concerns the application of the MLP structure. The MLP network is characterized by an extremely simple mathematical description, thanks to which it is used in the case of mechanical damages: bearing damage [121,231], eccentricity [231], and in the case of the damage to the stator electrical circuits [215][216][217][218], [224], [225], [232], supply voltage unbalance [219], [221], [227] or stator phase loss [221], [226], and demagnetization faults [232].…”

Section: B Shallow Neural Network Application In Pmsm Drivesmentioning

confidence: 99%

“…The most common form of using FFT in the event of electrical circuit faults is the popular MCSA [218]. The authors in [217] showed that the configuration of the input vector in the form of the simultaneous application of the results of FFT and statistical analysis made it possible to obtain a high efficiency of fault detection of the PMSM stator windings. The combination of spectral analysis with additional methods of signal processing was also presented in [120], [121].…”

Section: B Shallow Neural Network Application In Pmsm Drivesmentioning

confidence: 99%

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Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSM) are currently very topical due to the increasing use of these drives in safety-critical devices. Every year, more and more articles on this subject are published. Therefore, the aim of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods using the analysis of control structure signals for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of PMSM drives failures. Based on the presented literature analyses, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

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Section: B Shallow Neural Network Application In Pmsm Drivesmentioning

confidence: 99%

Section: B Shallow Neural Network Application In Pmsm Drivesmentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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“…In the practical implementation of an NN-based fault detection system, methods based on the application of training data from a mathematical model for both training and validation are often used [20,21]. However, such an approach is characterized by a very high training accuracy and high dynamics of the training process but relatively low effectiveness when testing the network on a real object.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Neural Fault Detectors of Permanent Magnet Synchronous Motor Trained With Symptoms From Field-Circuit Modeling

2022

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Permanent magnet synchronous motors (PMSMs) are increasingly used in industrial drive applications. However, these motors can also undergo various failures, causing production line downtime and resulting in economic loss. Modern diagnostic systems allow the analysis of technical conditions based on a dataset containing fault symptoms. In most cases, the development of diagnostic patterns indicates the necessity for interference in motor mechanical construction. This fact speaks to the use of mathematical models, particularly those based on finite-element methods. This study investigated the possibility of fault classification using self-organizing Kohonen maps and a multilayer perceptron, based on training with data from a field-circuit model. The objective of this study is to demonstrate that such neural systems can detect and classify real motor faults under different operating conditions. Research has focused on stator-winding faults, demagnetization, and mixed faults. Experimental tests demonstrated the impressive diagnostic capability of the shallow neural networks developed for diagnostic tasks.

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“…Among ANNs, the neural networks with a classical (shallow) structure and those based on deep learning (DL) can be distinguished. The MultiLayer Perceptron (MLP) is one of the most commonly used ANN types in an electric motor fault diagnosis [34][35][36]. The Radial Basis Function (RBF) ANN and Self Organizing Maps (SOMs) are also verified in this field of research [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

Pietrzak

Wolkiewicz

2022

Sensors

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Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology.

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An Intelligent Diagnostic Method for Permanent Magnet Synchronous Motors (PMSM) in the Electric Drive of Autonomous Vehicles

Cited by 13 publications

References 12 publications

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Effectiveness of Neural Fault Detectors of Permanent Magnet Synchronous Motor Trained With Symptoms From Field-Circuit Modeling

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

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