A Fault Prediction Of Equipment Based On CNN-LSTM Network

Abstract

Unit equipment is the key to industrial production, and predicting unit failures is the focus of improving equipment productivity. In order to improve the accuracy and reliability of predicting, and consider the use of multiple related influencing factors for prediction. This paper presents a fault prediction method based on CNN-LSTM. Firstly, the data of multiple variables that affects the predicted value over a period of time are formed into a large matrix. Then, the Long short-term memory network is trained by the feature information extracted from the convolutional neural network. This can predict device data at future time points and build models that use large amounts of data for predicting. Finally, the rationality and effectiveness of the proposed method are verified by the comparison between the evaluation example and the LSTM network.