A rotating machinery fault diagnosis method using composite multiscale fuzzy distribution entropy and minimal error of convex hull approximation
Xianzhu Zhao, Junsheng Cheng, Ping Wang, Yu Yang · Measurement Science and Technology · 2020
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.