Bearing fault diagnosis method in nuclear power plants based on EWT-GG clustering

被引：0
作者：
Wang Z. [1 ]
Xia H. [1 ]
Zhu S. [1 ]
Peng B. [1 ]
机构：
[1] Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2020年 / 41卷 / 06期
关键词：
Empirical wavelet transform; Fault diagnosis; Feature extraction; Fuzzy clustering; K-L divergence; Lempel-Ziv complexity; Nuclear power plant; Rotary machine;
D O I：
10.11990/jheu.201905063
中图分类号：
学科分类号：
摘要：
To improve the fault diagnosis accuracy of rotating equipment in nuclear power plants, a fault diagnosis method combining empirical wavelet transform (EWT) and Gath-Geva (GG) clustering is proposed. First, the EWT is used to decompose the bearing vibration signal to obtain a series of AM-FM components, and the Kullback-Leibler divergence is combined to select the main components containing the signal characteristic information. The sample entropy and Lempel-Ziv complexity of components are calculated as the eigenvectors of the signals, which are inputted into the GG clustering for the analysis and derive the classification results. The experiments show that compared with EWT-FCM, EWT-GK, and FMD-GG clustering algorithms, the classification performance of this method is more satisfying, which can provide a reliable and effective method for the fault diagnosis of rotating equipment in nuclear power plants. © 2020, Editorial Department of Journal of HEU. All right reserved.
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页码：899 / 906
页数：7
相关论文
共 17 条
  • [1] IMMOVILLI F, BELLINI A, RUBINI R, Et al., Diagnosis of bearing faults in induction machines by vibration or current signals:a critical comparison[J], IEEE transactions on industry applications, 46, 4, pp. 1350-1359, (2010)
  • [2] LUO Xiaohui, XU Dezhong, ZHAO Liang, Et al., Fault analysis of bearings in start-up pumping unit of CPR1000 nuclear power plant, General machinery, 7, pp. 66-68, (2015)
  • [3] CHANG Yuan, HUANG Xiaojin, LI Chunwen, Frequency band variance and energy operator for bearing fault diagnosis in nuclear power plant, Atomic energy science and technology, 48, 8, pp. 1458-1463, (2014)
  • [4] EKER S, AYAZ E, TVRKCAN E., Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery[J], Engineering applications of artificial intelligence, 16, 7, pp. 647-656, (2003)
  • [5] WANG C C, KANG Yuan, SHEN Pingchen, Et al., Applications of fault diagnosis in rotating machinery by using time series analysis with neural network[J], Expert systems with applications, 37, 2, pp. 1696-1702, (2010)
  • [6] LI Zipeng, CHEN Jinglong, ZI Yanyang, Et al., Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive, Mechanical systems and signal processing, 85, pp. 512-529, (2017)
  • [7] XU Yuanbo, CAI Zongyan, DING Kai, An enhanced bearing fault diagnosis method based on TVF-EMD and a high-order energy operator[J], Measurement science and technology, 29, 9, (2018)
  • [8] XIANG Ling, GUO Pengfei, GAO Nan, Et al., Rolling bearing fault diagnosis based on IITD and FCM clustering, Journal of aerospace power, 33, 10, pp. 2553-2560, (2018)
  • [9] LIU Changliang, WU Yingjie, ZHEN Chenggang, Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering, Proceedings of the CSEE, 35, 13, pp. 3358-3365, (2015)
  • [10] GILLES J., Empirical wavelet transform[J], IEEE Transactions on signal processing, 61, 16, pp. 3999-4010, (2013)
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