A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.