A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

Abstract

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.