The invention relates to a
Bayesian network (BN)-based rolling bearing fault diagnosis method. According to a common rolling bearing fault diagnosis method, a
mathematical model is required to be established, and an initial diagnosis effect is unsatisfactory; problems of the selection of a
wavelet base function are unsolved; and the
interpretability of a deduction process is low. The method comprises the following steps of: sampling a vibration
signal of a bearing, acquiring a sample, performing N-point rapid Fourier transformation
processing to convert a time-domain
signal into a frequency-domain
signal, calculating a fault characteristic vector, discretizing the fault characteristic vector, establishing a fault diagnosis reasoning BN model, setting a fault sample to be diagnosed, acquiring an observational evidence of the bearing, finishing updating the reliability Theta of a fault
diagnosis type node Bearing in the BN model, calculating a fault
diagnosis type node, and outputting a result. A complex mathematical modeling process for the vibration signal is avoided, an obtained diagnosis reasoning model has the advantages of a few characteristic parameters, prominent fault characteristics, high
interpretability and the like, and an effective way for solving the problems of the rolling bearing fault diagnosis is provided.