Rolling bearing residual life prediction method and system based on iterative correlation vector machine

Abstract

The invention belongs to the technical field of mechanical detection, and discloses a method and system for predicting the remaining life of a rolling bearing based on an iterative correlation vector machine. On the basis of the original correlation vector machine model, a plurality of correlation vector machine prediction models are generated serially through iteration; using The correlation vector machine explores the mapping relationship between the feature vector F and the residual life sequence T, establishes the RVM model and optimizes the model parameter σ through grid search, and its model is RVM 0 (F; σ 0 ); through iterative residual optimization, train a set of RVM models. Compared with methods such as neural networks, fewer samples are required and the calculation speed is faster. Compared with the original correlation vector machine model, the accuracy rate of the method provided by the invention is higher.

Description

technical field [0001] The invention belongs to the technical field of mechanical detection, and in particular relates to a method and system for predicting the remaining life of a rolling bearing based on an iterative correlation vector machine. Background technique [0002] At present, the existing technology commonly used in the industry is as follows: rolling bearings are one of the most important components in rotating machinery equipment, and their performance status directly affects the operation status of the entire equipment. Life is one of the important indicators to measure the performance of rolling bearings, but the actual data shows that the service life of rolling bearings has great dispersion. Rolling bearing health monitoring and residual life prediction are extremely necessary. Accurate residual life prediction can detect rolling bearing damage and deterioration trends as early as possible, provide data support for formulating economical and reasonable mai...

AI Technical Summary

Problems solved by technology

However, for complex mechanical systems, it is difficult to establish accurate models

The second type is based on data-driven models, which obtain the physical characteristics of bearings such as vibration and temperature in real time, and rely on empirical knowledge to establish stochastic models or fuzzy mapping to predict the remaining life. The disadvantage is that a large amount of historical data is required, and the prediction error is relatively large.

[0005] (1) It is difficult to establish an accurate model based on the method of predicting the life of the physical model

[0006] (2) The method of predicting life based on a data-driven model requires a large amount of historical data, and the prediction error is large

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