PCA-LSTM bearing residual life prediction method based on multilayer grid search

Abstract

The invention discloses a PCA-LSTM bearing residual life prediction method based on multilayer grid search, and the method comprises the steps: firstly extracting a plurality of time-frequency domainfeatures of bearing fault time sequence data, employing PCA to fuse a plurality of feature index quantities, and removing the redundant data of the feature indexes; obtaining required influence faultprincipal component data, namely a group of new comprehensive index time series data, preprocessing the time series data, converting the time series data into equipment degradation degree value data,and inputting the equipment degradation degree value data into a constructed LSTM model to perform fault sequence prediction training; achieving optimal selection of LSTM model parameters with the minimum prediction loss as the target through a multi-layer grid search algorithm, so that an optimal time series data prediction model is obtained, and finally the remaining service life of the bearingis obtained through polynomial curve fitting calculation. Problems of low prediction precision and low prediction speed of bearing life prediction are solved, and the stability and accuracy of bearingresidual life prediction are improved.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a bearing, in particular to a PCA-LSTM method for predicting the remaining life of a bearing based on multi-layer grid search. Background technique [0002] Today's society is in an era of technological revolution and development, and machinery and equipment are increasingly developing in the direction of large-scale, precision, intelligence, automation and systematization. Bearings are an important part of mechanical equipment. Once a bearing fails, it will inevitably affect the operation of mechanical equipment, which will not only cause immeasurable economic losses to enterprises and society, but also lead to catastrophic casualties. During the use of bearings, the remaining service life (RUL) will gradually decrease, which greatly increases the potential for failure. Therefore, if the RUL of the bearing can be accurately predicted, it will be of great significance for the predi...

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Problems solved by technology

[0004] Purpose of the invention: Aiming at the problem that the prediction accuracy of the current remaining life is not high, the present invention proposes a PCA-LSTM bearing remaining life prediction method based on multi-layer grid search, so as to obtain the current accurate remaining service life of the equipment at any time when the bearing is running

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