Qiang Li, Yong-Sheng Qi, Xue-Jin Gao, Yong-Ting Li, Li-Qiang Liu. A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines. International Journal of Automation and Computing. https://doi.org/10.1007/s11633-021-1305-0
Citation: Qiang Li, Yong-Sheng Qi, Xue-Jin Gao, Yong-Ting Li, Li-Qiang Liu. A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines. International Journal of Automation and Computing. https://doi.org/10.1007/s11633-021-1305-0

A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines

doi: 10.1007/s11633-021-1305-0
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  • Author Bio:

    Qiang Li received the B. Eng. degree in control science and engineering from Inner Mongolia University of Technology, China in 2008. He is a master student of control science and engineering in Inner Mongolia University of Technology, China. His research interests include condition evaluation, fault monitoring and fault diagnosis of wind power system. E-mail: 18447070345@163.comORCID iD:0000-0002-8505-8556

    Yong-Sheng Qi received the B. Eng. degree in control science and engineering from Inner Mongolia University of Technology, China in 1999, and the Ph. D. degree in engineering from University of technology of Beijing, China in 2011. From 2011, he has been working as a professor at Institute of Electric Power, Inner Mongolia University of Technology, China. He is a member of China Artificial Intelligence Association and the reviewers for Chinese journals, including Control Theory and Application, Control Engineering. His research interests include fault monitoring and diagnosis for complex industrial processes, condition evaluation, fault monitoring and fault diagnosis of wind power system, and cooperative control technology for mobile robots. E-mail: qys@imut.edu.cn (Corresponding author)ORCID iD: 0000-0002-6059-7035

    Xue-Jin Gao received the B. Eng. degree in control science and engineering from Hebei University of Science and Technology, China in 1997, and the Ph. D. degrees in engineering from University of technology of Beijing, China in 2006. He has published 2 monographs and more than 50 academic papers, of which more than 50 have been indexed by SCI, EI and ISTP. He has won the first prize twice and a third prize of provincial science and technology progress, and obtained 15 national invention patents and 3 utility model patents. His research interests include prediction of key variables in industrial processes, engineering process condition monitoring and fault diagnosis, and biosensor development.E-mail: gaoxuejin@bjut. edu.cn

    Yong-Ting Li received the B. Eng. degree in automation from Taiyuan University of Technology, China in 1998, and the M. Sc. degree in aircraft design from Beijing University of Aeronautics and Astronautics, China in 2005. She has published three software copyrights, and more than 10 papers, including 4 EI indexed papers. Her research interests include condition assessment, fault monito ring and fault diagnosis of wind turbine system, research on wavelet signal analysis algorithm.E-mail: liyt@imut.edu.cn

    Li-Qiang Liu received the B. Eng. degree in control science and engineering from Inner Mongolia University of Technology, China in 1997, and the Ph. D. degree in electrical engineering from Xi′an Jiaotong University, China in 2010. He has been working as a professor at Center of Electrotechnics Teaching, Inner Mongolia University of Technology, China. His research interests include circuit theory and its application, substation grounding grid reliability and fault diagnosis.E-mail: llqiang@imut.edu.cn

  • Received Date: 2021-02-01
  • Accepted Date: 2021-04-29
  • Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed, which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a “W” structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect. Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference, and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.

     

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