Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

Abstract

This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may lead to missing fault information. Secondly, as the scale increases, the original sequence is gradually shortened, leading to instability and inaccuracy in entropy estimation when dealing with short-term sequences. The improved coarse-grained calculation method overcomes its limitations to improve stability, and the HE method extracts deep fault frequency information from the high and low-frequency components of the multivariate signal. Then, the extracted features are dimensioned using the Max-Relevance Min-Redundancy (mRMR) to create a new set of fault features to improve diagnosis efficiency. Finally, the Support Vector Machine(SVM) determines the degree and type of fault. Experiments were conducted with three examples, the results show that IMHMDE can effectively extract the feature information according to mechanical faults’ features and improve the efficiency of fault diagnosis.