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A novel fault diagnosis method based on EMD, cyclostationary, SK and TPTSR

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Abstract

A novel method based on empirical model decomposition (EMD), cyclostationary, spectral kurtosis (SK) and two-phase test sample sparse representation (TPTSR), called ECK-TPTSR is proposed for fault diagnosis in this paper. In the ECK-TPTSR method, the vibration signal is decomposed into several components by EMD. Then each component can be modelled as cyclostationary for noise reduction. Next, the proposed method computes the kurtosis of the unbiased autocorrelation on the squared envelope of each component, and extracts the component with the highest kurtosis. Finally, the extracted component is used to construct training samples and test samples, which are input into the TPTSR classifier to fulfill fault classification accurately. Moreover, the experimental results indicate that the ECK-TPTSR method can effectively achieve fault diagnosis of motor bearing and obtain higher classification accuracy.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (51376028, 61771087), the National Key Technology R&D Program (2015BAF20B02), Traction Opening Project of State Key Laboratory of Electric Power, Southwest Jiaotong University (TPL2002), Liaoning Natural Science Foundation (20170540145), Liaoning Provincial Department of Education Research Fund Project (JDL2019025), and Liaoning BaiQianWan Talents Program.

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Authors and Affiliations

  1. College of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, China

    Yijie Niu

  2. College of Software Engineering, Dalian Jiaotong University, Dalian, 116028, China

    Yijie Niu & Yuanyuan Li

  3. College of Locomotive and Rolling, Dalian Jiaotong University, Dalian, 116028, China

    Jiyou Fei

  4. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Deng Wu

  5. Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu, 610031, China

    Deng Wu

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  1. Yijie Niu
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  2. Jiyou Fei
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  3. Yuanyuan Li
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  4. Deng Wu
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Corresponding author

Correspondence to Jiyou Fei.

Additional information

Recommended by Editor No-cheol Park

Yijie Niu received her M.S. degree from Jilin University of in 2005. Now, she is a lecturer and Ph.D. candidate in Dalian Jiaotong University. Her main research interests include artificial intelligence, object tracking and fault diagnosis.

Jiyou Fei, Professor, received his Ph.D. from Xian Jiaotong University in 2003. His research interests focus on automatic control and advanced measurement.

Wu Deng, Professor, received the Doctor degree in computer science and technology from Dalian Maritime University in 2012. His research interests include artificial intelligence, fault diagnosis.

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Niu, Y., Fei, J., Li, Y. et al. A novel fault diagnosis method based on EMD, cyclostationary, SK and TPTSR. J Mech Sci Technol 34, 1925–1935 (2020). https://doi.org/10.1007/s12206-020-0414-y

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  • DOI: https://doi.org/10.1007/s12206-020-0414-y

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