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학술지 Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network
Cited 43 time in scopus Download 9 time Share share facebook twitter linkedin kakaostory
저자
Pangun Park, Piergiuseppe Di Marco, Hyejeon Shin, 방준성
발행일
201911
출처
Sensors, v.19 no.21, pp.1-17
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s19214612
초록
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 detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.
키워드
Autoencoder, Fault detection, Fault diagnosis, Long short-term memory, Rare event, Time delay
KSP 제안 키워드
Combined approach, Convolution neural network(CNN), Critical components, Deep convolutional neural networks, Faulty Data, Integrated learning, LSTM network, Learning ability, Learning approach, Long short-term memory network, Low-dimensional