A Novel Weak Feature Extraction Method for Rotating Machinery: Link Dispersion Entropy

被引:4
|
作者
Ding, Li [1 ]
Ji, Jinchen [2 ]
Li, Yongbo [3 ]
Wang, Shun [3 ]
Noman, Khandaker [4 ]
Feng, Ke [5 ]
机构
[1] Northwestern Polytech Univ, Sch Mech & Engn, Xian 710072, Peoples R China
[2] Univ Technol Sydney, Sch Mech & Mechatron Engn, Sydney, NSW 2007, Australia
[3] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China
[4] Northwestern Polytech Univ, Sch Civil Aviat, Xian 710072, Peoples R China
[5] Natl Univ Singapore, Dept Ind Syst Engn & Management, Singapore 11576, Singapore
基金
中国国家自然科学基金;
关键词
Complexity evaluation; fault diagnosis; feature extraction; link dispersion entropy (LDE); rotating machinery; MULTISCALE FUZZY ENTROPY; FAULT-DIAGNOSIS; TIME-SERIES; SCHEME; MODEL;
D O I
10.1109/TIM.2023.3312483
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The entropy-based feature extraction is a promising tool for extracting weak features from rotating machinery. However, the existing research has paid little attention to the state transition process, which brings the problem of accuracy and comprehensiveness in complexity estimation. To address this issue, this article proposes link dispersion entropy (LDE) based on the theory of the Markov chain for weak feature extraction. By calculating the transition probability of symbol patterns, the LDE can extract the fault information contained in the transition, enabling it to capture the early weak fault. Furthermore, LDE is extended to a multiscale analysis by combining it with the coarse-gaining process for comprehensive feature extraction, termed multiscale LDE (MLDE). Finally, three simulated signals and two different experimental data are utilized to verify the advantage of MLDE in extracting the weak fault features. Results demonstrate that MLDE has the best performance in fault diagnosis of rotating machinery compared with the existing five methods, namely sample entropy (SE), fuzzy entropy (FE), permutation entropy (PE), dispersion entropy (DE), and symbolic dynamic entropy (SDE).
引用
收藏
页数:12
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