Chinese Journal of Mechanical Engineering >

2017 , Vol. 30 >Issue 6: 1314 - 1324

DOI: https://doi.org/10.1007/s10033-017-0188-z

ORIGINAL ARTICLE

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

  • Guo-Qian Jiang ,
  • Ping Xie ,
  • Xiao Wang ,
  • Meng Chen ,
  • Qun He
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  • 1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
    2 The Six Branch of Qinhuangdao Port Co, Ltd, Qinhuangdao 066004, China

收稿日期: 2016-11-30

  修回日期: 2017-09-29

  网络出版日期: 2019-07-16

基金资助

Supported by Hebei Provincial Natural Science Foundation of China (Grant No. F2016203421).

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Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

  • Guo-Qian Jiang ,
  • Ping Xie ,
  • Xiao Wang ,
  • Meng Chen ,
  • Qun He
Expand
  • 1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
    2 The Six Branch of Qinhuangdao Port Co, Ltd, Qinhuangdao 066004, China

Received date: 2016-11-30

  Revised date: 2017-09-29

  Online published: 2019-07-16

Supported by

Supported by Hebei Provincial Natural Science Foundation of China (Grant No. F2016203421).

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摘要

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies:motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

关键词： Intelligent fault diagnosis; Vibration signals; Unsupervised feature learning; Sparse filtering; Multiscale feature extraction

本文引用格式

Guo-Qian Jiang , Ping Xie , Xiao Wang , Meng Chen , Qun He . Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning[J]. Chinese Journal of Mechanical Engineering, 2017 , 30(6) : 1314 -1324 . DOI: 10.1007/s10033-017-0188-z

Abstract

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies:motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Key words： Intelligent fault diagnosis; Vibration signals; Unsupervised feature learning; Sparse filtering; Multiscale feature extraction

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