Phase-phase Short Fault Analysis of Permanent Magnet Synchronous Motor in Electric Vehicles

Wang, Zhifu; Yang, Jingzhe; Cao, Chuang; Gu, Zhenghui

doi:10.1016/j.egypro.2016.06.112

Cited by 8 publications

(7 citation statements)

References 5 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%

“…In addition, the feedback affects the dynamics of the network, in which a change in the state of one of the neurons affects the operation of the entire network. The main representative of recursive structures in the field of technical diagnostics is the Elman network [225] which is characterized by partial recursion (one-step delays).…”

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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“…The use of classic neural structures, the training process of which is based on the results of mathematical modeling while verification takes place on a real PMSM, is rarely described in the literature [31][32][33]. This results from the difficulties in the development of NN input vectors that are resistant to motor operating conditions and measurement noise.…”

Section: Pmsm Fault Classifiers Based On Shallow Neural Networkmentioning

confidence: 99%

“…This results from the difficulties in the development of NN input vectors that are resistant to motor operating conditions and measurement noise. In most cases, these systems include only PMSM damage detection [32,33] without fault classification. Furthermore, the analyses presented in the literature mainly cover cases of very serious failures that, in practice, result in the shutdown of PMSM [31], for example, phase failure [32] or phase-to-phase short circuits [33].…”

Section: Pmsm Fault Classifiers Based On Shallow Neural Networkmentioning

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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“…Accordingly, during the development of NN-based fault detectors, one should have empirical knowledge about the selection and preparation of training and testing data. Currently, the most commonly used diagnostic applications are based on classic neural structures, such as a multilayer perceptron [13][14][15], self-organising Kohonen networks [9,17], recurrent NNs [14,18] and a radial-basis function NN [12,14,19]. Although this approach to the diagnostic system design provides a relatively high accuracy in detecting damages, expanding the neural structures with the analytical methods to isolate the symptoms of damage does not eliminate the disadvantages resulting from the preprocessing of the measured signal.…”

Section: Introductionmentioning

confidence: 99%

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

Skowron

Orłowska-Kowalska

Kowalski

2021

IET Electric Power Appl

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Permanent magnet synchronous motors (PMSM) have become one of the most substantial components of modern industrial drives. These motors, like all the others, can unfortunately undergo various failures, causing production line downtime and resulting losses. Accordingly, it is necessary to develop fault diagnostic techniques which detect the damages at the earliest possible stage. This study presents a method of detecting incipient faults of the PMSM stator windings using direct signal analysis and a convolutional neural network (CNN). During the tests, the structures of CNN were optimised to constitute a balance between the high efficiency of fault detection and a small number of network parameters. The effectiveness of the CNNs with inputs constituted by different electrical signals measured in the drive system is compared. Three raw data signals are tested as CNN inputs, namely: stator phase currents, phase-to-phase voltages and axial flux, without data preprocessing. The article aims to show the possibility of detecting the incipient interturn short circuits in the PMSM stator winding based on the information obtained directly from the measured signals as well as to present the influence of the drive operating conditions and the type of measurement signals used on the structure and performance of the developed CNNs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Phase-phase Short Fault Analysis of Permanent Magnet Synchronous Motor in Electric Vehicles

Cited by 8 publications

References 5 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

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

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