| Summary: | The rotating machinery possesses complicated structures and various fault types, whose health state monitoring is essential for the normal production and operation of the equipment. To distinguish different working states of rotating machinery efficiently and accurately, this article presents a novel approach for extracting fault features of vibration signals called modified hierarchical multiscale dispersion entropy (MHMDE). And on this basis, an innovative approach for fault diagnosis of rotating machinery based on MHMDE, multi-cluster feature selection (MCFS) and particle swarm optimization kernel extreme learning machine (PSO-KELM) is developed. Firstly, MHMDE is employed to extract the high-dimensional fault features of rotating machinery. This approach can effectively overcome the shortcomings that multi-scale entropy only focuses on the information in the low-frequency components but discards the high-frequency information, as well as the significant dropping of efficiency if the number of hierarchical layers of hierarchical entropy is large. Then MCFS is employed to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive feature vectors are input into the PSO-KELM-based fault classifier to complete the rotating machinery fault diagnosis. It is proved that the presented approach can effectively identify different fault states of rotating machinery through three typical examples. Meanwhile, the presented approach is compared with multi-scale dispersion entropy (MDE) and hierarchical dispersion entropy (HDE), etc. The results show that the presented approach possesses more superior performance.
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