Method for identifying health degradation state of rolling bearing

Abstract

The invention relates to a method for identifying a health degradation state of a rolling bearing. The method comprises the steps of: (1), obtaining a historical monitoring signal of the rolling bearing, pre-processing the monitoring signal, eliminating a singular value, and reducing noise; (2), respectively performing time-domain analysis, power spectrum analysis and CEEMDAN decomposition on thepre-processed monitoring signal, so that time-domain, power spectrum and intrinsic mode energy characteristics are obtained; (3), training a CSVM model by utilizing various characteristics of the monitoring signal; and (4), for the online real-time acquired monitoring signal of the rolling bearing, inputting the various characteristics obtained in the step (2) into the CSVM model trained in the step (3), so that the current health degradation state identification result of the rolling bearing is obtained. According to the method in the invention, the health degradation state of the rolling bearing can be identified precisely in real time; real-time monitoring on the state of the rolling bearing is realized; and thus, safe, steady and long-period operation of a numerical control machine tool is ensured.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing detection, in particular to a method for identifying the health degradation state of rolling bearings. Background technique [0002] Rolling bearings are one of the most common components of rolling bearings, and their working conditions directly affect the reliability and safety of the entire rolling bearing. Once the rolling bearing fails, the rolling bearing will stop, and various abnormal phenomena such as loss of function will occur, and even cause major safety accidents. Therefore, the development of rolling bearing state identification is of great practical significance for improving the maintenance efficiency of rolling bearings, reducing their maintenance costs, and ensuring their long-term stable operation. [0003] Support Vector Machine (Support Vector Machine, SVM) is a classic data-driven classification method, which has many unique advantages in solving small sample, nonlin...

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Problems solved by technology

However, the large computational burden of the support vector machine kernel function limits its application on large-scale data sets

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