Rolling bearing remaining life prediction method based on long-short term memory network

Abstract

The invention provides a rolling bearing remaining life prediction method based on a long-short term memory network. The rolling bearing remaining life prediction method comprises the steps that characteristics of abrasion signals of the rolling bearing is extracted; principle component analysis is conducted on the extracted characteristics to obtain fusion characteristics; normalization processing is conducted on the fusion characteristics; cyclic overlapped interception is conducted on fusion characteristic data in a set step-size to obtain short sequences; the short sequences are divided into a training set and a prediction set; an LSTM deep learning network is constructed; the LSTM deep learning network is trained through the training set; the LSTM deep learning network is verified through the prediction set; and training results and prediction results are subjected to inverse normalization processing and output. According to the rolling bearing remaining life prediction method based on the long-short term memory network, an LSTM prediction model is provided based on the field of deep learning and has high applicability and accuracy in fault time sequence analysis, and the problem of long-term dependence in time sequence is solved.

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 a long-short-term memory network. Background technique [0002] For rolling bearings with high reliability and safety requirements, it is very important to effectively predict the reliability index in the use stage. At present, many methods have been used to solve the reliability prediction problem, and these methods can be roughly divided into three categories: (1) The method based on the failure mechanism (physics-of-failure, PoF) PoF is a method based on the intrinsic (2) data-driven method (data-driven, DD), DD is a method of directly predicting reliability indicators by applying statistics or machine learning and other technical means; (3 ) fusion method, which is a combination of PoF and DD. In recent years, data-driven methods have been widely ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for predicting the remaining life of rolling bearings based on long-short-term memory network, which is based on the field of deep learning, and the proposed LSTM prediction model has strong applicability and higher accuracy in fault time series analysis , which solves the long-run dependency problem in time series

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