Noise-robust compound fault diagnosis of rolling bearing using a sparse bayesian framework with interpretable prior knowledge

Abstract In industrial operational environments, rolling bearing vibration signals not only contain fault related periodic impulse components, but also interference noises. The coupling of these signals attenuates the fault characteristics, particularly in cases of compound faults, significantly affecting the diagnostic accuracy of bearings. This paper proposes a rolling bearing fault separation and compound diagnosis method using a sparse Bayesian framework with adaptive prior knowledge. Firstly, the proposed method segments signals into a finite number of modes, employing filter banks to adaptively select decomposition modes with a new compound fault indicator, which reduces noise interference and eliminates irrelevant components while ensuring the preservation of fault-related information. Secondly, by employing an interpretable strategy enables fault frequency estimation, eliminating errors from empirical parameter settings and avoiding the computational complexity of parameter optimization. The feature frequencies are estimated based on the envelope harmonic product spectrum and used as a prior knowledge of sparse Bayesian learning. Finally, to avoid the restriction of current feature extraction methods that primarily focus on the selection and extraction of a single demodulation frequency band, a sparse Bayesian probabilistic model is particularly designed for compound fault diagnosis. Simulation and experimental results show that the proposed method can effectively extract and separate the fault features of each individual modes and realize the diagnosis of rolling bearings.