Roller bearing service life predicting model of self-adaptive multi-kernel combination relevance vector machine

Abstract

The invention relates to a roller bearing service life predicting model of a self-adaptive multi-kernel combination relevance vector machine. The roller bearing service life predicting model comprises the following steps of utilizing particle filter to initialize a combination kernel function weighting matrix, so as to obtain a combination kernel function set; establishing a multi-kernel combination relevance vector machine set; performing iteration predicting, weight updating and re-sampling self-adaption to obtain an optimal multi-kernel combination relevance vector machine model; finally, predicting the running state and remaining life of the rolling bearing. The roller bearing service life predicting model has the advantages that the excellent characteristics of a plurality of single kernel functions are adaptively integrated, the reliance of the single kernel function relevance vector machine model on the parameters is reduced, the predicting accuracy is improved, the predicting stability is better, the robustness of the model is higher, and the engineering application value is higher.

Description

Technical field [0001] The invention relates to the technical field of the operating state and life prediction of rolling bearings, in particular to a rolling bearing life prediction model of an adaptive multi-core combined correlation vector machine. Background technique [0002] Rolling bearings are widely used in rotating machinery and other equipment, and their health is directly related to the safe operation of mechanical equipment. Because rolling bearings are often working in harsh environments such as high speed and heavy loads, they are prone to failure or even failure. Once the rolling bearing fails or fails, it will inevitably pose a serious threat to the safe service of mechanical equipment, ranging from production accidents that cause mechanical equipment to shutdown, and at worst, leading to major disasters such as machine destruction and death. Because the effective life of each rolling bearing varies greatly, the traditional regular maintenance strategy is not on...

AI Technical Summary

Problems solved by technology

However, the current kernel function selection of correlation vector machine mainly relies on experience, and mostly uses a single kernel function, which leads to a great increase in the dependence of the prediction accuracy of the correlation vector machine model on parameters.

Similarly, since different single kernel functions have different characteristics and have different sensitivities to different trend data, the single kernel function correlation vector machine model established by using a single kernel function has low prediction accuracy stability and weak robustness

Even in a small number of combined kernel function research, there are shortcomings, that is, two kernel functions are artificially selected for combination, the number of combined kernel functions cannot be automatically selected, and the performance of the fused kernel function is limited.

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