4.6 Article

Soft Fault Diagnosis Using URV-LDA Transformed Feature Dictionary

期刊

IEEE ACCESS
卷 9, 期 -, 页码 16019-16029

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3051409

关键词

Fault diagnosis; fault dictionary; linear discriminant analysis; and electromagnetic relay

资金

  1. National Research and Development Program of China [2017YFB1300800]
  2. National Natural Science Foundation of China [61671172]

向作者/读者索取更多资源

This article develops a new type of dictionary URV-LDA dictionary by combining the unit residual signal vector and the linear discriminant analysis for feature transformation, which can better solve the soft faults issues with significant increases on diagnostic accuracy in electromechanical systems.
Dictionary-based fault diagnosis methods, focusing on storing feature patterns of known faults, have been widely used for electromechanical systems. The state of component degradation caused soft faults, however, are continuously changeable. Thus, conventional dictionaries cannot be applied for diagnosis of soft faults with multi-degradation levels. To address this issue, this article develops a new type of dictionary by combining the unit residual signal vector (URV) and the linear discriminant analysis (LDA) for feature transformation, which is referred to as URV-LDA dictionary. The unit residual signal vector keeps the fault feature growth trends but eliminates the degradation severity influence. The linear discriminant analysis is then implemented to find the best projection directions for classification. Specifically, two dictionaries named as the URV-MLDA binary-value dictionary and the URV-SLDA unique-value dictionary are proposed. To validate the efficiency of two developed dictionaries, an electromagnetic relay is carried out and two conventional methods are compared. The comparison results show the developed dictionaries can better solve the soft faults issues with significant increases on diagnostic accuracy.

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