摘要：The fault diagnosis technique is of important for the safety operation of the rotating machinery. In the fault diagnosis framework, the entropy-based method is a promising tool for the feature extraction and signal processing. Among the entropy-based methods, the diversity entropy has arisen increasing attention due to its merits of high consistency, strong robustness, and high calculation efficiency. However, it suffers the defect that the multiscale procedure leads to unstable complexity estimation at higher scales. This induces a poor cluster performance in analyzing the compound mechanical fault signals. To address this issue, this paper presents a novel feature extraction method called composite multiscale diversity entropy (CMDE). The proposed CMDE utilizes the mean complexity value of multiple sliding windows for each scale to enhance the stability, which enables the diversity entropy could dig richer fault information from deeper scales for the compound fault diagnosis of rotating machinery. Then, the stability of CMDE has been evaluated using synthetic gear signals. At last, the proposed CMDE has been applied in the compound mechanical fault diagnosis. The experimental results show that the CMDE achieves the highest diagnosis accuracy compared to the existing entropy-based feature extraction methods.
