Rolling bearing state identification method based on empirical mode decomposition (EMD) and principal component analysis (PCA)

Abstract

The invention discloses a rolling bearing state identification method based on empirical mode decomposition (EMD) and principal component analysis (PCA) and belongs to the technical field of rail transit. The method includes the following steps: (1) obtaining experiment data; (2) conducting two-category state experiment data partitioning or four-category state experiment data partitioning; (3) conducting EMD processing on each partitioned section of data respectively, obtaining intrinsic mode function (IMF) component of each section of data, and forming an IMF matrix of each section of data; (4) extracting statistical characteristic component of the rolling bearing state; (5) determining a safety margin boundary; and (6) identifying a rolling bearing operation state. The rolling bearing state identification method has the advantages of providing a rolling bearing operation state safety margin estimation method based on EMD-PCA-least square support vector machine (LSSVM) and an identification method of normal and various failure states and enabling safety margin accuracy rate and various state identification rate to be both larger than 95%. The rolling bearing state identification method can monitor and diagnose rolling bearing failure fast and effectively.

Description

technical field [0001] The invention belongs to the technical field of rail traffic safety. The invention relates to a rolling bearing state identification method based on EMD and PCA. Background technique [0002] In rail vehicles, automobiles, and construction machinery industries, rolling bearings are widely used, but at the same time, the failure rate is also high. According to statistics, only 10% to 20% of rolling bearings can reach the design life. Therefore, accurate and effective monitoring and identification of rolling bearing operation status is of great significance for improving work efficiency, reducing operating costs, and ensuring equipment operation safety. [0003] Feature extraction and state identification are the key issues to be solved in rolling bearing condition monitoring, and scholars at home and abroad have conducted in-depth research on this. In terms of feature extraction, Empirical Mode Decomposition (EMD) is a relatively new signal processing...

AI Technical Summary

Problems solved by technology

However, none of the above studies considered the statistical characteristics of the vibration signal, and various statistics of the vibration signal often contain a wealth of information about the operating state of the object, which can detect changes in its operating state

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