Bearing fault diagnosis method based on morphological top hat product filtering of optimal scale

Abstract

The invention discloses a bearing fault diagnosis method based on morphological top hat product filtering of an optimal scale. The diagnosis method comprises: mounting an acceleration sensor near thebearing to acquire bearing vibration signals and determine the initial range of the structural element scale; performing morphological top hat product filtering on the bearing vibration signals at each structural element scale to obtain morphological filtering results of a plurality of scales; calculating the third-order accumulation amount diagonal slice and the diagonal slice spectrum of the morphological filtering result of each scale to obtain third-order accumulative amount diagonal slices and diagonal slice spectra of a plurality of scales; calculating the fault characteristic ratio of the diagonal slice spectrum of each scale and determining a diagonal slice spectrum of the optimal scale according to the maximum fault characteristic ratio criterion; extracting the bearing fault feature information from the diagonal slice spectrum of the optimal scale to realize the accurate discrimination of bearing fault types. The bearing fault diagnosis method based on morphological top hat product filtering of an optimal scale is simple and feasible, overcomes the defects of traditional multi-scale morphology analysis, and can improve the diagnosis precision of bearing faults.

Description

technical field [0001] The invention belongs to the technical field of bearing fault diagnosis, and in particular relates to a bearing fault diagnosis method based on top-hat product filtering of optimal scale form. Background technique [0002] As an important joint of mechanical equipment, rolling bearings will be subjected to alternating loads during equipment operation. As time goes by, the operating status of the bearings will inevitably change, thereby affecting industrial production. Most failures in mechanical equipment are closely related to damage to bearing components. According to relevant statistics, in the key parts of mechanical equipment, the failure rate of equipment caused by bearing damage is as high as 30%, about 20% of the failures in gearboxes are from the damage of bearings, and the damage rate of bearings in motors is also as high as 40%. . Therefore, rolling bearings are considered to be one of the most prone to damage parts in mechanical equipment...

AI Technical Summary

Problems solved by technology

[0004] The present invention overcomes the disadvantages of low feature extraction accuracy in traditional multi-scale morphological analysis, and at the same time possesses the multi-resolution analysis function of multi-scale morphological analysis, and avoids affecting fault diagnosis results by selecting structural element scales based on human experience in the past problem, a bearing fault diagnosis method based on top-hat product filter of optimal scale shape is provided

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