Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation

Abstract

Incipient faults features are often extremely weak and susceptible to heavy noise, making it challenging to obtain the concentrated faulty energy ridges in the time-frequency domain. Thus, a novel impulse-enhanced sparse time-frequency representation (IESTFR) method is proposed in this paper. First, the time-rearranged multisynchrosqueezing transform is utilized to produce a time-frequency representation with a high energy concentration for faulty impulses. Next, a new non-convex penalty function is constructed by the hyperbolic tangent function, which can enhance the periodic impulsivity of sparse time-frequency representation for more obvious fault characteristic frequency. Moreover, the time-frequency transform is evaluated and compared by simulated signals and a selection strategy for the regularization parameter is designed. Simulated signals and two experimental signals are applied to verify the effectiveness of IESTFR, and the results show that IESTFR is effective and superior in bearing incipient fault feature extraction.

A new rolling bearing fault diagnoses method based on period-doubling bifurcation in the Hindmarsh–Rose model

“…Furthermore, artificial intelligence techniques have gained significant traction in the realm of fault diagnosis. For instance, diverse approaches, such as those based on deep transfer learning and multi-sensor fusion, have been deployed to bolster the accuracy of fault diagnosis [21][22][23].…”