Fault detection method and system based on wavelet transform and neural network, and medium

Abstract

The invention discloses a fault detection method and system based on wavelet transform and a neural network, and a medium. The method comprises the steps of: acquiring a first vibration signal of a to-be-detected bearing; determining first frequency domain information of the first vibration signal, and selecting a corresponding wavelet basis function according to the first frequency domain information; performing continuous wavelet transform on the first vibration signal according to a wavelet basis function to obtain a first time-frequency diagram; inputting the first time-frequency diagram into a pre-trained fault diagnosis model, and outputting to obtain a fault type identification result, wherein the fault diagnosis model is obtained through convolutional neural network training. According to the fault detection method of the invention, by selecting the appropriate wavelet basis function, weak fault vibration information can be reserved in continuous wavelet transform, interference of noise is avoided, the precision of the time-frequency diagram is improved, and the accuracy degree of bearing fault detection is further improved; and the fault diagnosis model is trained through the convolutional neural network, the bearing fault detection efficiency is improved, and the method can be widely applied to the technical field of equipment bearing fault detection.

Description

technical field [0001] The invention relates to the technical field of equipment bearing fault detection, in particular to a fault detection method, system and medium based on wavelet transform and neural network. Background technique [0002] The vibration characteristics of bearing faults in different stages are different. For the earliest ultrasonic stage, the vibration energy is not high, and the characteristics are not obvious. When the bearing failure is close to the end in the later stage of the fault, the fault characteristic frequency of the bearing and The natural frequency is swamped by random broadband high frequency "vibration noise". Therefore, the existing vibration processing methods for rolling bearing faults focus more on the second and third stages, namely, the natural frequency stage and the fault characteristic frequency stage. However, for early bearing faults, due to the weak fault vibration signal, there is still a lack of an efficient and accurate b...

AI Technical Summary

Problems solved by technology

However, for the early faults of bearings, due to the weak fault vibration signal, there is still a lack of an efficient and accurate bearing fault detection method.

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