Rolling bearing fault detection method based on actual measurement signal

Abstract

The invention discloses a rolling bearing fault detection method based on an actual measurement signal. The rolling bearing fault detection method comprises the steps of: firstly, converting a rollingbearing fault time domain vibration signal to an angle domain through employing an order tracking technology; secondly, carrying out parameter optimization on variational mode decomposition through adpopting a longicorn beard search algorithm, and decomposing all state vibration signals of a rolling bearing to obtain a series of intrinsic mode functions, wherein frequency band energy in differentintrinsic mode functions can change when different faults happen to the bearing; thirdly, extracting Renyi entropy features from modal components containing main fault information, and constructing afeature subset; and finally, using normal state vibration signals easy to obtain for training, extracting fault characteristic quantities, establishing fault data samples and incremental learning data samples, acquiring a fault recognition model through training by adopting a single-class support vector machine incremental learning algorithm, judging whether the rolling bearing breaks down or notaccurately, and achieving fault early warning.

Description

technical field [0001] The invention belongs to the technical field of electrical fault detection, and in particular relates to a rolling bearing fault detection method based on measured signals. Background technique [0002] As the core component of wind turbines, rolling bearings are constantly affected by impacts and loads due to the harsh working environment of the turbines. At the same time, due to the intermittent and strong fluctuation of wind speed, wind turbines are faced with complex working conditions. In the scenario of variable working conditions, the fault characteristics of the vibration signal of the fan rolling bearing are unstable, the working conditions are complex, and the fault sample data is small, which makes it difficult to analyze the bearing fault. After long-term operation, the bearings of wind turbines are prone to failure. Once there is a problem with the bearings, it will cause noise and abnormal noise, and it will cause the collapse of the tran...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a rolling bearing fault detection method based on measured signals, which solves the problems of difficulty in extracting fault features and low recognition accuracy in the prior art based on bearing signals of variable working conditions and unbalanced small samples

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