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作者:

Luo, Hongwei (Luo, Hongwei.) | Song, Liuyang (Song, Liuyang.) | Wang, Mengyang (Wang, Mengyang.) | Wang, Huaqing (Wang, Huaqing.) | Cui, Lingli (Cui, Lingli.) (学者:崔玲丽)

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EI

摘要:

In order to solve the difficulty of features extraction of compound faults in underdetermined state, this research proposes an approach to extract signal features by combining adaptive generalized S transform (GST) and non-negative matrix factorization algorithm (NMF). The adaptive function (AF) is introduced to optimize GST. The optimized GST is used to process monitored signals to get the time-frequency features matrix. The NMF is improved by Itakura-Saito (IS) divergence. And the dimensionality of the signal time-frequency matrix is reduced by it. After iterative updating, several low-dimensional matrices are obtained. The time-domain waveforms of low-dimensional matrices are reconstructed, and the envelope spectrum analysis is performed to realize compound faults diagnosis. The simulation test and the actual bearing compound fault signals experiment prove that this method can effectively extract compound fault features in underdetermined state and realize bearing compound faults diagnosis. © 2019 IEEE.

关键词:

Extraction Factorization Fault detection Iterative methods Mathematical transformations Matrix algebra Roller bearings Signal processing Spectrum analysis Time domain analysis

作者机构:

  • [ 1 ] [Luo, Hongwei]Beijing University of Chemical Technology, School of Mechanical Electrical Engineering, Chao Yang District, Beijing; 100029, China
  • [ 2 ] [Song, Liuyang]Beijing University of Chemical Technology, School of Mechanical Electrical Engineering, Chao Yang District, Beijing; 100029, China
  • [ 3 ] [Wang, Mengyang]Beijing University of Chemical Technology, School of Mechanical Electrical Engineering, Chao Yang District, Beijing; 100029, China
  • [ 4 ] [Wang, Huaqing]Beijing University of Chemical Technology, School of Mechanical Electrical Engineering, Chao Yang District, Beijing; 100029, China
  • [ 5 ] [Cui, Lingli]Beijing University of Technology, Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Chao Yang District, Beijing; 100124, China

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年份: 2019

语种: 英文

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WoS核心集被引频次: 0

SCOPUS被引频次: 1

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