Rolling bearing detection method based on LMD (Local Mean Decomposition) and gray correlation

Abstract

The invention discloses a rolling bearing detection method based on LMD (Local Mean Decomposition) and gray correlation, belongs to a rolling bearing fault detection method in the field of mechanical engineering, and relates to a rolling bearing detection method based on a fuzzy entropy algorithm of LMD (Local Mean Decomposition) and gray correlation. The method comprises the steps: employing an acceleration sensor to collect vibration acceleration signals of a rolling bearing during operation, wherein the vibration acceleration signals comprise a no-fault normal bearing vibration acceleration signal and inner ring, rolling body or outer ring fault bearing vibration acceleration signals. carrying out the LMD decomposition of the collected acceleration signals, and obtaining a plurality of PF (product function) components and residual errors; calculating the gray correlation degree of a test sample and a standard matrix through employing a gray correlation algorithm, and carrying out the fault mode recognition. The method can effectively carry out the feature extraction of the vibration signals, solves problems that the EMD decomposition is severe in excessively modal mixing and end effect and a PF component is large in data size and cannot serve as a characteristic vector, and achieves the effective recognition of the operation state of the rolling bearing.

Description

technical field [0001] The invention belongs to a rolling bearing fault detection method in the field of mechanical engineering, and in particular relates to a rolling bearing fault detection method based on a local mean decomposition fuzzy entropy algorithm and gray correlation. Background technique [0002] Rolling bearings are very important components in mechanical equipment, and are widely used in various fields such as daily life, industrial production, and national defense construction. The running state of the rolling bearing directly affects the stability, reliability and life of the whole equipment. Therefore, the condition monitoring and fault diagnosis technology of rolling bearings play a very important role in safe production, reducing economic losses, and ensuring the safe and stable operation of machinery. [0003] Because rolling bearings are affected by the working environment, most of the fault signals of rolling bearings are non-stationary and nonlinear ...

AI Technical Summary

Problems solved by technology

Using the rolling bearing detection method based on LMD fuzzy entropy algorithm and gray correlation, it overcomes the phenomenon that EMD decomposition has serious over-modal aliasing and endpoint effects, and the problem that the PF component data is too large to be used as a feature vector, and realizes the effective identification of rolling bearing operating status

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