Rolling bearing fault diagnosis method

Abstract

The invention brings forward a rolling bearing fault diagnosis method based on improved fractal box dimensions and a self adaption gray correlation theory. Fault characteristics (including useful and easy-to-differentiate information capable of reflecting different working states of a bearing) are extracted from bearing vibration signals via the improved fractal box dimensions, and fault types and different orders of severity of bearings can be automatically identified via self adaption gray correlation algorithms. The rolling bearing fault diagnosis method is used for solving a problem that work and health states of rolling bearings cannot be easily assessed via traditional time domain and frequency domain methods; via the rolling bearing fault diagnosis method, fault types and different orders of severity of different bearings can be accurately and effectively identified.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method based on a multi-fractal dimension algorithm and an adaptive gray correlation theory algorithm. Background technique [0002] As an important component, rolling bearings are widely used in almost all types of rotating machinery. Rolling bearing failure is one of the most important causes of failure and damage of rotating machinery, and brings huge economic losses. In order to ensure reliable operation of the unit and reduce economic losses, it is extremely necessary to develop a reliable and effective rolling bearing fault diagnosis method. Among the many bearing fault diagnosis methods, the diagnosis method based on vibration signal has received extensive attention in the past few decades. [0003] The vibration signal of the bearing contains rich information on the health status of the machine, which also makes it possible to extract the dominant features representing the health stat...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is: It is difficult to make an accurate assessment of the working health status of rolling bearings by using traditional time domain and frequency domain methods

Images