Feature Extraction of Uniform Demagnetization Faults Based on Joint Simulation

This paper investigates the uniform demagnetization fault of permanent magnet synchronous motors through joint simulation. The joint simulation is built by Ansys Electronics Desktop and Simulink, which can simulate the finite element model of the motor with control methods. In Ansys Electronics Desktop, the average value of the dq axis inductance of the motor is calculated through a finite element model. In Simulink, a sliding mode observer was used to estimate the back electromotive force and rotational speed. The estimated rotational speed value was compared with the simulated rotational speed value, verifying the accuracy of the sliding mode observer. According to the simulation results, two types of fault features were extracted separately in transient and steady-state conditions.

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Published
2024-10-11
DOI
https://doi.org/10.1109/phm-beijing63284.2024.10874477
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Feature Extraction of Uniform Demagnetization Faults Based on Joint Simulation

Fan Zhang, Gang Niu
Radiomics and Machine Learning in Medical Imaging
article

Feature Extraction of Uniform Demagnetization Faults Based on Joint Simulation

Fan Zhang, Gang Niu
article en

Abstract

This paper investigates the uniform demagnetization fault of permanent magnet synchronous motors through joint simulation. The joint simulation is built by Ansys Electronics Desktop and Simulink, which can simulate the finite element model of the motor with control methods. In Ansys Electronics Desktop, the average value of the dq axis inductance of the motor is calculated through a finite element model. In Simulink, a sliding mode observer was used to estimate the back electromotive force and rotational speed. The estimated rotational speed value was compared with the simulated rotational speed value, verifying the accuracy of the sliding mode observer. According to the simulation results, two types of fault features were extracted separately in transient and steady-state conditions.

Tongji University (CN)
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Radiomics and Machine Learning in Medical Imaging
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