Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Jankowska, Kamila; Dybkowski, M.

doi:10.3390/designs6010018

Cited by 19 publications

(10 citation statements)

References 27 publications

(32 reference statements)

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“…So far, different methods have been introduced for sensor fault detection; however, several existing limitations are bringing attention to data-driven and machine learningbased methods. In [132], various current sensor fault detection was investigated using NN and raw speed and current signals. Comparing the conventional methods, using this NN increased the detection speed during low computational burden.…”

Section: Recent Data-driven Fdd Methods For Different Pmsm Motor Driv...mentioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

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Section: Recent Data-driven Fdd Methods For Different Pmsm Motor Driv...mentioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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“…Artificial neural networks (ANNs) are used e.g. for control (see [3][4][5][6][7][8][9][10][11][12][13][14][15][16] to name a few) or for state and parameter estimation (see, e.g., [17][18][19][20][21][22][23][24][25][26][27]). The main goals of ANN-based algorithms are (i) increasing accuracy, (ii) improving robustness and (iii) reducing computational effort based on data-driven approaches in contrast to physics-based approaches (although both can and should be combined).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.

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“…Several works have presented the detection of current sensor damage using methods based on artificial intelligence (AI). Those papers describe the detection of an induction motor [25][26][27]; such a solution for PMSM has been described only in simulation studies by the authors of this work [28].…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

Jankowska

Dybkowski

2023

Electronics

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In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented.

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Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Cited by 19 publications

References 27 publications

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

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