Intelligent fault identification of rotary machinery using refined composite multi-scale Lempel-Ziv complexity

被引:52
作者
Li, Yongbo [1 ]
Wang, Shun [1 ]
Deng, Zichen [1 ]
机构
[1] Northwestern Polytech Univ, Sch Aeronaut, MIIT Key Lab Dynam & Control Complex Syst, Xian 710072, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Lempel-Ziv complexity; Rotary machinery; Intelligent fault diagnosis; Feature extraction; EMPIRICAL MODE DECOMPOSITION; SYMBOLIC DYNAMIC ENTROPY; DIAGNOSIS; METHODOLOGY; PROGNOSTICS; MANAGEMENT;
D O I
10.1016/j.jmsy.2020.05.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel-Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.
引用
收藏
页码:725 / 735
页数:11
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