Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract: Aiming at the problem that it is difficult for the traditional fault diagnosis method to extract effective features from QAR data, this paper proposed a dual-channel fusion model, CNN-LSTM, which combines the Convolutional Neural Network(CNN) and the Long Short-Term Memory(LSTM) . Respectively as two channels, CNN and LSTM fused through the attention mechanism to make the model able to simultaneously express the features of the data in both space dimension and time dimension and verified the validity of the feature extraction of the fusion model through time series prediction. Results of the experiment show that when compared with single CNN or LSTM, the dual-channel fusion model can extract data features more effectively, make the errors of both single-step prediction and multi-step prediction reduce by an average of 35.3%, and provide a new research idea for fault diagnosis based on QAR data.