Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3039614
Keywords
Autoencoder; deep learning; fault detection; industrial process; long short-term memory
Funding
- National Natural Science Foundation of China [71777173]
- Action Plan for Scientific and Technological Innovation of Shanghai Science and Technology Commission [19511106303]
- Fundamental Research Funds for the Central Universities
Ask authors/readers for more resources
This article proposes a new deep neural network, CLSTM-AE, for feature learning from process signals in modern industrial processes. By embedding selective residual block in the deep network, it improves the training accuracy and performs feature selection effectively. The hybrid learning technique with convolutional LSTM and autoencoder provides a new way for feature learning and fault detection for complex 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 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available