Rolling bearing fault damage degree identification method

Abstract

The invention discloses a rolling bearing fault damage degree identification method comprising the steps of fault vibration signal acquisition, calculating the mathematical morphological gradient spectral value, calculating the change rate of the mathematical morphological gradient spectral value, determining the optimal scale range of the structural elements, calculating the high-order differencemathematical morphological gradient spectral value, calculating the high-order difference mathematical morphological gradient spectral entropy, defining the fault damage degree of discrimination, calculating the fault damage degree of discrimination and judging the fault damage degree. The damage degree of the bearing inner race fault can be effectively identified so that the method has high correct rate of damage degree identification and can greatly enhance the identification efficiency and is an effective fault degree quantitative identification method, and a new method can be provided forrotating machinery fault damage degree identification and fault prediction and the method is great in practicality and worthy of popularization.

Description

technical field [0001] The invention belongs to the technical field of fault detection of mechanical parts, and in particular relates to a method for identifying the fault damage degree of a rolling bearing. Background technique [0002] Rolling bearings are important components in rotating machinery. Working in harsh environments for a long time, under the joint influence of load bearing, transmission, impact and other loads, they are prone to failures such as bearing fatigue spalling, pitting, and severe plastic deformation in the contact area, which in turn leads to machine failure. Accidents such as breakage and outage occur frequently, so it is very necessary to carry out fault monitoring, state analysis and diagnosis of rolling bearings. [0003] The evolution of bearing faults has a development process from mild to severe, and fault quantitative diagnosis is an effective description method to realize the fault evolution process. Existing diagnostic methods can be sum...

AI Technical Summary

Problems solved by technology

Most of these analysis methods are qualitative analysis of bearing faults, that is, to determine whether the fault exists and the type of fault, while the research on quantitative diagnosis of bearing faults, that is, to determine the degree of fault damage and the remaining life is relatively small, and traditional mathematics The shape spectrum is difficult to accurately describe the shape complexity of the signal, and the insufficient ability to distinguish fault modes leads to unsatisfactory identification of fault damage degrees.

Images