Data-Driven Diagnosis of PMSM Drive with Self-Sensing Signal Visualization and Deep Transfer Learning

Zheng Li, Fengxiang Wang, Haotian Xie, Dongliang Ke, Tinglan Ye, S. Alireza Davari, Ralph Kennel

Research output: Contribution to journalArticlepeer-review

Abstract

Harmonics in the three-phase currents of PMSM systems are caused by inverter non-linearity, mismatched controller parameters, and sampling errors, which increase torque ripples. To address this issue, this paper proposes a data-driven diagnosis method based on self-sensing signal visualization and deep learning. A comprehensive database was established by amalgamating data from simulations and experiments, encompassing a wide range of fault categories. Using self-sensing current signals as the original data, without requiring external test instruments. The short-time Fourier transform is used to obtain the spectrum image of current. And the spectrum images of the three-phase currents are combined into a single image to reduce the data dimension using image fusion. After classifying and labeling the data, the samples were trained using SqueezeNet's transfer learning. The test results show that the fault diagnosis accuracy of 99.03 percent. Compared with traditional methods, the method proposed in this paper realizes system-level fault diagnosis and is more practical.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Energy Conversion
DOIs
StateAccepted/In press - 2023

Keywords

  • Fault diagnosis
  • Fault diagnosis
  • Feature extraction
  • Harmonic analysis
  • Permanent magnet synchronous motor
  • Synchronous motors
  • Time-frequency analysis
  • Torque
  • Transfer learning
  • Transforms

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