Bayesian network-based rolling bearing fault diagnosis method

Abstract

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.

Description

technical field [0001] The invention relates to a fault diagnosis method using characteristic signals for modeling and reasoning, in particular to a rolling bearing fault diagnosis method based on a Bayesian network. Background technique [0002] Rolling bearings are one of the most important mechanical parts in rotating machinery. They are widely used in chemical industry, metallurgy, electric power, aviation and other important departments. At the same time, they are also one of the most vulnerable components. The performance and working conditions of bearings directly affect the performance of the shafts associated with them, the gears installed on the shafts, and even the entire machine equipment. Their defects will cause abnormal vibration and noise of the equipment, and even cause equipment damage. Therefore, it is particularly important to diagnose rolling bearing faults, especially for the analysis of early faults, and to avoid accidents in production practice. [...

AI Technical Summary

Problems solved by technology

However, it is a complex and difficult task to use signal features for fast and accurate fault diagnosis in noisy, uncertain, and dynamic environments.

Images