The Remaining Life Prediction Algorithm Of Medium-carbon Steel Based On Auto-Encoder And Convolutional Neural Network

With the development of data science,more and more researchers have begun to try to use deep learning technology for life prediction.The key to the remaining life prediction task is to accurately predict the degradation process of the equipment,and then derive the remaining life based on the degradation trend.Therefore,the analysis of historical life data becomes very important.Only the process of accurately analyzing performance degradation from historical data can achieve more accurate residual life prediction.Deep learning,as a data-driven technology,does not require an accurate mathematical model and is well suited for big data analysis.Based on this background,this paper studies the prediction of remaining life based on convolution autoencoder and convolutional neural network.The main research contents include:(1)This paper reviews the residual life prediction method based on deep learning technology,and focuses on the application of convolutional neural network and cyclic neural network in the field of residual life prediction.The loss function and evaluation index of life prediction model based on deep learning technology are elaborated in detail.(2)A residual life prediction algorithm based on convolution autoencoder and convolutional neural network is proposed.The model pre-features the input signal through a convolutional auto-encoder,and then uses the convolution network to further feature extraction of the signal and regress the relationship between the feature and the output.By adding the damage ratio feature,this paper simplifies the learning process of the network and improves the accuracy of the model.(3)The tensile fatigue test of medium carbon steel sheets was designed and implemented.In the fatigue test of this paper,a medium carbon steel plate is used as a test piece,and a load far less than the yield limit strength is applied by a tensile fatigue tester to simulate a high cycle fatigue process of the material at a low stress level.At the same time,a data acquisition system based on LAbVIEW was written for data acquisition.(4)In this paper,the acoustic emission signal data collected in the fatigue test of medium carbon steel is taken as the data set.The performance of the proposed model is evaluated.The effects of some techniques used in this paper on performance are analyzed.The experimental results show that the residual life prediction algorithm proposed in this paper has better accuracy.By predicting the remaining life of medium carbon steel sheets based on the residual life prediction algorithm based on convolution autoencoder and convolutional neural network,the combination of traditional fatigue accumulation theory and depth network is studied,which provides a more accurate study of residual life prediction algorithm.A way of thinking.