Method for predicting remaining service life of rolling bearing integrated with KELM

Abstract

The invention discloses a method for predicting the remaining service life of a rolling bearing integrated with the KELM (Kernel Extreme Learning Machine), and belongs to the technical field of the bearing service life prediction. The method is used to solve the problem that the prediction of the remaining service life of the rolling bearing has difficulty in prediction and low prediction accuracy. The method firstly extracts features of a vibration signal based on the variational mode decomposition, introduces a new similarity dimension reduction method for features dimension reduction, and further extracts the features-CEF (Cyclic Enhancement Features) with strong monotonicity, similarity, and stability. Multiple KELM models are constructed through that the CEF extracted by the multiplebearings is used as the input of the KELM, the ratio of the current service life to the whole life, p, that is, the life percentage is used as the output. A prediction model integrated with KELM is constructed by combining the random forest to obtain a current prediction result p value. The CEF of the test bearing is input into the prediction model, the current p value is predicted, and the secondorder exponential smoothing method is used for fitting to predict the RUL of the bearing. The experimental verification shows that the proposed prediction method has higher prediction accuracy than other literatures.

Description

technical field [0001] The invention relates to a method for predicting the remaining service life of a rolling bearing, and belongs to the technical field of bearing life prediction. Background technique [0002] Rolling bearings are an important part of rotating machinery. Due to the complex working environment and operating conditions, more than 30% of mechanical failures in rotating machinery are caused by faulty bearings. Therefore, rolling bearings are also one of the most vulnerable parts in rotating machinery. [1,2] . Accurate prediction of rolling bearings (Remaining Useful Life, RUL) can provide a basis for preventive maintenance decisions, prolong the life cycle of equipment, improve the reliability and utilization of the whole machine, and avoid accidents [3] . [0003] At present, many scholars at home and abroad have studied the feature extraction method of vibration signals of rolling bearings. Literature [4] uses envelope analysis combined with multi-scale...

AI Technical Summary

Problems solved by technology

[0006] Different rolling bearings have different operating conditions, environments, and use requirements, and their performance degradation characteristics have the disadvantages of weak monotonicity, weak similarity, and poor stability, and the single prediction model has the problem of poor robustness, resulting in the remaining service life of the bearing ( Remaining Useful Life, RUL) prediction accuracy is not high

The present invention solves the problems of difficulty in prediction (the data of variable working conditions are used in the present invention) and low prediction accuracy in existing prediction of remaining service life of rolling bearings

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