TY - JOUR
T1 - A Novel Weak Feature Extraction Method for Rotating Machinery
T2 - Link Dispersion Entropy
AU - Ding, Li
AU - Ji, Jinchen
AU - Li, Yongbo
AU - Wang, Shun
AU - Noman, Khandaker
AU - Feng, Ke
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - 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).
AB - 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).
KW - Complexity evaluation
KW - fault diagnosis
KW - feature extraction
KW - link dispersion entropy (LDE)
KW - rotating machinery
UR - http://www.scopus.com/inward/record.url?scp=85171547357&partnerID=8YFLogxK
U2 - 10.1109/TIM.2023.3312483
DO - 10.1109/TIM.2023.3312483
M3 - 文章
AN - SCOPUS:85171547357
SN - 0018-9456
VL - 72
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 3532012
ER -