Adaptive fast iterative filter Holo-spectrum analysis and its applications to fault diagnosis of rolling bearing

Abstract

As a signal demodulation analysis technique, Holo–Hilbert spectral analysis (HHSA) excels in capturing the intricate cross-scale coupling dynamics present in nonlinear and non-stationary vibration signals. Nonetheless, HHSA suffers from a lack of rigorous mathematical foundation, is subject to modal mixing constraints, and exhibits limited noise robustness. To address the aforementioned issues, this study presents an innovative nonlinear and non-stationary signal demodulation technique, referred to as adaptive fast iterative filter Holo-spectrum analysis (AFIFHSA). Also, an adaptive fast iterative filtering (AFIF) algorithm incorporated within AFIFHSA is designed to dynamically achieve a nonlinear and non-stationary signal decomposing. From that, several approximate narrowband signals, possessing physical significance at an instantaneous frequency, and a trend term can be obtained. Furthermore, the marginal spectrum (MS) obtained by AFIFHSA can be utilized to represent the effectiveness of fault characteristic identification. Lastly, the simulation and measured data are utilized to showcase AFIFHSA’s exceptional capabilities in recognizing high-resolution and eximious modulation relationships. The analysis outcomes additionally illustrate that AFIFHSA, as proposed, showcases superior performance in fault identification and robustness with comparison to other conventional approaches.