Hierarchical dispersion Lempel–Ziv complexity for fault diagnosis of rolling bearing

The fault information of rolling bearings is generally contained in vibration signals. How to efficiently unearth fault information from the raw signals is the key to detecting and evaluating the health condition of mechanical equipment. Therefore, a hierarchical dispersion Lempel–Ziv complexity (HDLZC) feature extraction method is developed in this paper to improve the accuracy of fault diagnosis. In this method, dispersion theory addresses the deficiency of Lempel–Ziv complexity, and can obtain more fault features from the raw signal. Second, the hierarchical extraction of high- and low-frequency components from time series can improve the ability to describe dynamic features. Simulations and experiments respectively demonstrate the predominance of HDLZC. The experimental results reveal that this method is significantly better than multiscale dispersive Lempel–Ziv complexity, hierarchical Lempel–Ziv complexity, multiscale dispersion entropy, and multiscale permutation entropy in extracting fault information.