Prediction Method of Remaining Life of Rolling Bearing Based on Feature Fusion and Particle Filter

Abstract

Disclosed is a rolling bearing remaining life prediction method based on feature fusion and particle filtering. According to an index calculation process, firstly, original features are extracted from bearing vibration signals, the extracted original features are clustered by the adoption of a relevance clustering method, then, one typical feature is selected from each cluster to form optimal feature sets, and finally the feature sets are fused by the adoption of a weight fusion method into a final recession index. According to a life prediction process, firstly, smoothing and resampling are carried out on the recession index, the time interval is adjusted to be an expected value, state-space model initial parameters are calculated by the adoption of least square fitting, then, model parameters are updated in real time according to new observation data, and finally the remaining life of a bearing can be predicted. According to the rolling bearing remaining life prediction method based on feature fusion and particle filtering, the difference between the life prediction result and a true value is small, and the application effect is good.

Description

technical field [0001] The invention belongs to the technical field of prediction of the remaining life of rolling bearings, and in particular relates to a method for predicting the remaining life of rolling bearings based on feature fusion and particle filtering. Background technique [0002] Rolling bearings are widely used in rotating machinery, and their health is directly related to the safe operation of mechanical equipment. Because rolling bearings often work in harsh environments with high speed and heavy loads, failures such as wear and fatigue pitting occur from time to time. Once the bearing fails, it will inevitably pose a serious threat to the safe operation of the equipment, ranging from production accidents that cause equipment downtime to serious disasters that cause machine crashes and human deaths. Due to the great difference in the effective life of each rolling bearing, the traditional regular maintenance strategy is not only time-consuming and laborious...

AI Technical Summary

Problems solved by technology

Complex operating conditions and changing external environment make the initial parameters of each bearing state model vary greatly, and the artificially set model parameters often cannot effectively describe its decline trend, resulting in a large difference between the life prediction result and the real value, and the application effect Poor, which limits the application of the particle filter method in the prediction of the remaining life of rolling bearings

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