A rotating machinery fault diagnosis method using composite multiscale fuzzy distribution entropy and minimal error of convex hull approximation. (1st December 2020)

• Record Type: Journal Article
• Authors: Zhao, Xianzhu; Cheng, Junsheng; Wang, Ping; Yang, Yu
• Is Part Of: Measurement science & technology. Volume 32:Number 2(2021)
• Publication Date: 2020-12-01
• Subjects: rotating machinery -- fault diagnosis -- composite multiscale fuzzy distribution entropy -- minimal error of convex hull approximation; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/abbd11
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Rotating machinery plays an increasingly crucial role in mechanical systems. For its normal operation, a novel fault diagnosis method is proposed in this paper, using composite multiscale fuzzy distribution entropy (CMFDE) and minimal error of convex hull approximation (MECHA). In this paper, CMFDE is utilized to extract essential information and measure time series complexity for vibration signals. Results indicate the CMFDE has less information loss and better stability. Then, to fulfill the classification tasks, the first several main features obtained by principal components analysis are fed into the proposed MECHA-based classifier. Results show MECHA has better classification performance. Using the laboratory data, we validate the feasibility and superiority of the proposed fault diagnosis method through two cases consisting of different fault types or fault severity degrees.