Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network.

ABSTRACT: Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event de

SUBMITTER: Park P 

PROVIDER: S-EPMC6866134 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature