Remaining Life Prediction Method of Rolling Bearings Based on Convolutional Long Short-Term Memory Recurrent Neural Network

Abstract

The invention provides a method for predicting the remaining life of a rolling bearing based on a convolutional long-short-term memory cyclic neural network, which includes: acquiring the vibration acceleration signal of the rolling bearing as a data sample; establishing a convolutional autoencoder, using the data sample as an input, and training to obtain multiple Layer convolutional self-encoder; obtain the characteristic data of the data sample according to the data sample and the multi-layer convolutional self-encoder; use the fuzzy C-means algorithm to determine the health evaluation index of the rolling bearing according to the characteristic data of the data sample; establish the convolution length The time-memory cyclic neural network model uses the bearing data samples in the recession period and the corresponding health assessment indicators as the input of the convolution long-short-term memory cyclic neural network, and trains to obtain a prediction model; the invention reduces the dependence on manual experience and professional knowledge, and effectively The amount of data input into the long-short-term memory recurrent neural network is greatly reduced, the calculation is simplified, and the prediction model can comprehensively and effectively reflect the decline state of the bearing.

Description

technical field [0001] The invention relates to the technical field of life prediction of rolling bearings, in particular to a method for predicting the remaining life of rolling bearings based on a convolutional long-short-term memory cyclic neural network. Background technique [0002] As an important part of modern manufacturing, bearings play a vital role in the reliable operation of most rotating machinery, directly affecting the health of mechanical systems. During the operation of mechanical equipment, the changes and uncertainties of complex working conditions will cause damage to rolling bearings, which directly affects the accuracy and reliability of the entire mechanical equipment system. By effectively monitoring the working state of rolling bearings and accurately predicting their degradation trends, the safe and reliable operation of mechanical equipment can be ensured and major economic losses can be avoided. Therefore, it is necessary to monitor the health o...

AI Technical Summary

Problems solved by technology

However, these feature quantities are only sensitive to a certain stage of the bearing degradation process. If a certain feature quantity is used alone as the degradation feature quantity of the bearing, the error of the prediction result is large

The comprehensive index of multiple features can describe the bearing degradation process relatively accurately, but increases the computational complexity

In addition, in most of the existing prediction models, the extraction of features still requires expert experience and manual extraction.

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