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Electrical Engineering

, Volume 101, Issue 3, pp 813–827 | Cite as

Application of hybrid dimensionality reduction for fault diagnosis of three-phase inverter in PMSM drive system

  • Shichuan Ding
  • Xueyi Li
  • Jun HangEmail author
  • Yinjiang Wang
Original Paper
  • 114 Downloads

Abstract

The feature dimension reduction is a useful pre-processing step for fault diagnosis, where the irrelevant and redundant information in the data can be reduced. In this case, not only the computation complexity can be reduced, but also the better classification performance can be obtained. Hence, this paper presents a multiple fault diagnosis method for the three-phase inverter in PMSM drive system, where the hybrid dimensionality reduction method is applied to reduce the dimension of the fault features. First, time-domain fault features are extracted from the line-to-line voltage signals. Second, a feature reduction is performed by combining principal component analysis and linear discriminant analysis, where the linear discriminant analysis is improved by introducing the singular value decomposition and redefining the between-class scatter which solve the issue of small sample and the problem that the similar classes are not easily separated. Finally, the faults are diagnosed by support vector machine. Both the simulation and the experimental results show that the proposed method can improve the discrimination performance of the fault types after the dimension reduction, making it in favor of fault classification.

Keywords

Permanent magnet synchronous machine Fault diagnosis Feature reduction Three-phase inverter Principal component analysis Linear discriminant analysis 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (51637001, 51607001) and the Natural Science Foundation of Anhui Province (1708085QE108).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shichuan Ding
    • 1
    • 2
  • Xueyi Li
    • 1
    • 2
  • Jun Hang
    • 1
    • 2
    Email author
  • Yinjiang Wang
    • 1
    • 2
  1. 1.School of Electrical Engineering and AutomationAnhui UniversityHefeiChina
  2. 2.National Engineering Laboratory of Energy-Saving Motor and Control TechniqueAnhui UniversityHefeiChina

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