Chinese Journal of Mechanical Engineering >

2020 , Vol. 33 >Issue 1: 16 - 16

DOI: https://doi.org/10.1186/s10033-020-00437-3

Intelligent Manufacturing Technology

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

  • Fei Shen ,
  • Chao Chen ,
  • Jiawen Xu ,
  • Ruqiang Yan
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  • 1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
    2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

收稿日期: 2019-03-06

  修回日期: 2020-01-18

  网络出版日期: 2020-05-18

基金资助

Supported by National Natural Science Foundation of China (Grant No. 51575102) and Jiangsu Postgraduate Research Innovation Program (Grant No. KYCX18_0075)

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A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

  • Fei Shen ,
  • Chao Chen ,
  • Jiawen Xu ,
  • Ruqiang Yan
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  • 1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
    2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Received date: 2019-03-06

  Revised date: 2020-01-18

  Online published: 2020-05-18

Supported by

Supported by National Natural Science Foundation of China (Grant No. 51575102) and Jiangsu Postgraduate Research Innovation Program (Grant No. KYCX18_0075)

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摘要

Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution. The short-time Fourier transform (STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion (BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent (GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest's Drivetrain Dynamics Simulator gear data sets were analyzed to verify the effectiveness of the proposed approach.

关键词： Gear fault diagnosis; Non-negative matrix factorization; Co-clustering; Varying working conditions

本文引用格式

Fei Shen , Chao Chen , Jiawen Xu , Ruqiang Yan . A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions[J]. Chinese Journal of Mechanical Engineering, 2020 , 33(1) : 16 -16 . DOI: 10.1186/s10033-020-00437-3

Abstract

Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution. The short-time Fourier transform (STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion (BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent (GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest's Drivetrain Dynamics Simulator gear data sets were analyzed to verify the effectiveness of the proposed approach.

Key words： Gear fault diagnosis; Non-negative matrix factorization; Co-clustering; Varying working conditions

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