期刊
JOURNAL OF PROCESS CONTROL
卷 87, 期 -, 页码 54-67出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2020.01.004
关键词
Multivariate process; Fault diagnosis; Convolutional auto-encoder; Feature learning; Tennessee Eastman Process; Fed-batch fermentation penicillin process
资金
- National Natural Science Foundation of China [71777173]
- Fundamental Research Funds for the Central Universities
- Shanghai Science and Technology Commission innovation science and technology action plan project [19511106303]
Noise and high-dimension of process signals decrease effectiveness of those regular fault detection and diagnosis models in multivariate processes. Deep learning technique shows very excellent performance in high-level feature learning from image and visual data. However, the large labeled data are required for deep neural networks (DNNs) with supervised learning like convolutional neural network (CNN), which increases the time cost of model construction significantly. A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is utilized to learn hierarchical feature representations through noise reduction of high-dimensional process signals. Auto-encoder integrated with convolutional kernels and pooling units allows feature extraction to be particularly effective, which is of great importance for fault detection and diagnosis in multivariate processes. The comparison between 1D-CAE and other typical DNNs illustrates effectiveness of 1D-CAE for fault detection and diagnosis on Tennessee Eastman Process and Fed-batch fermentation penicillin process. The proposed method provides an effective platform for deep-learningbased process fault detection and diagnosis of multivariate processes. (C) 2020 Elsevier Ltd. All rights reserved.
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