Signal enhancement method and system for bearing fault diagnosis

Abstract

The invention discloses a signal enhancement method for bearing fault diagnosis. The method comprises the following steps of acquiring fault time domain signal data of a bearing under different fault types, expanding the single time domain signal data by using a data enhancement and expansion technology to obtain enough deep learning training samples, and dividing the deep learning training samples into a training set, a verification set and a test set, constructing a two-dimensional fault image according to the training set, the verification set and the test set by using a transverse interpolation technology, and conducting histogram equalization processing on the two-dimensional fault image, so that the dynamic range of gray value difference between pixels can be increased, texture details and contrast can be highlighted, periodic fault features in the noisy image can be enhanced, and fault signal enhancement can be realized. Weak fault features in a strong interference environment can be highlighted, and the rolling bearing diagnosis accuracy can be improved.

Description

technical field [0001] The invention relates to the technical field of rolling bearing fault diagnosis and classification, in particular to a signal enhancement method and system for bearing fault diagnosis. Background technique [0002] Mechanical equipment is developing in the direction of large-scale and precision, and its intelligence is also increasing, which puts forward higher requirements for the reliability of equipment operation. As the core slewing bearing component in rotating machinery, slight defects on its surface may cause the operation failure of the entire device system, resulting in huge casualties and property losses. The traditional testing method for rolling bearings requires staff to regularly remove the bearings from the mechanical equipment for safety inspection, which consumes a lot of manpower and material resources and affects the industrial production process. The intelligent maintenance scheme uses advanced sensors and detection technologies to...

AI Technical Summary

Problems solved by technology

At the same time, due to the complexity of mechanical equipment, the vibration signals of each component of the system are modulated and superimposed on each other, resulting in the measured vibration signal often showing nonlinear and non-stationary characteristics. The traditional filtering and noise reduction method is used to process the signal, while filtering out the interference noise. It is inevitable to filter out some useful signals together, resulting in the loss of useful signals; and the fault characteristics of mechanical equipment are not obvious in the early stage, and are reflected in the signal very weakly. Therefore, the weak signal will be submerged in the strong background noise. The enhancement of fault information is an effective way to improve the accuracy of rolling bearing fault diagnosis

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