Convolutional Long Short-Term Memory Autoencoder-Based Feature Learning for Fault Detection in Industrial Processes

Process signals show the characteristics of large scale, high dimension, and strong correlation in modern industrial processes, which brings a big challenge for process fault detection and diagnosis. Due to the powerful feature learning ability, deep learning has been widely used in image and visual...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 70; S. 1 - 15
Hauptverfasser: Yu, Jianbo, Liu, Xing, Ye, Lyujiangnan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Autoencoder
Continuously stirred tank reactors
Convolution
Data mining
deep learning
Fault detection
Fault diagnosis
Feature extraction
industrial process
Logic gates
long short-term memory
Machine learning
Principal component analysis
Process monitoring
Signal processing
ISSN:0018-9456, 1557-9662
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Zusammenfassung:Process signals show the characteristics of large scale, high dimension, and strong correlation in modern industrial processes, which brings a big challenge for process fault detection and diagnosis. Due to the powerful feature learning ability, deep learning has been widely used in image and visual processing. This article proposes a new deep neural network (DNN), convolutional long short-term memory autoencoder (CLSTM-AE) for feature learning from process signals. The convolutional LSTM (ConvLSTM) is proposed to describe the distribution of the process data and learn effective features on time series data for fault detection. A selective residual block is embedded in the deep network to improve the training accuracy and perform feature selection from process signals. Two statistics, the T 2 and the squared prediction error (SPE), are generated in the feature space and residual space of CLSTM-AE, respectively. Finally, the feasibility and advantages of CLSTM-AE are shown on a simulated process, the Tennessee-Eastman process (TEP), and the continuous stirred tank reactor (CSTR). CLSTM-AE has good fault detection performance in these cases, which shows that it is capable of learning effective features from complex process signals. The hybrid learning technique with convolutional LSTM and autoencoder provides a new way for feature learning and fault detection for complex industrial processes.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3039614