A novel empirical random feature decomposition method and its application to gear fault diagnosis

Sparse random mode decomposition (SRMD) is an emerging signal decomposition method for analyzing time series data. However, SRMD is unable to adaptively select suitable clustering parameters, and its decomposition effect is easily affected by noise. To overcome the above deficiencies, a novel signal decomposition method named empirical random feature decomposition (ERFD) is proposed in this paper. Firstly, ERFD utilizes sparse random feature model to construct the random feature energy spectrum of input signal, and the extraction of signal components can be achieved via spectrum segmentation mechanism. Subsequently, an adaptive continuous envelope segmentation strategy guided by the mean harmonic intensity ratio (MHIR) index is designed for the adaptive frequency band division of random feature energy spectrum, which effectively attenuates the interference of noise extreme points and ensures the accurate separation of interested components. Finally, the random features corresponding to different frequency bands are reconstructed to obtain a series of mutually independent intrinsic random components (IRCs). Furthermore, the proposed ERFD method is applied to gear fault diagnosis, and its feasibility and effectiveness are fully verified through simulation and two experimental cases. The comparative analysis results indicate that ERFD has a better suppression effect on noise and can effectively extract weak fault features of gear.