期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 164, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2022.107884
关键词
Fault detection; Fault diagnosis; Process monitoring; Process safety; Smart fault detection and diagnosis; Deep learning
资金
- National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China [2018AAA0101605]
- National Natural Science Foundation of China [21878171]
Process fault detection and diagnosis is an essential tool for ensuring safe production in chemical industries. However, most current methods are not smart enough to handle the complex challenges in real industrial processes, resulting in a lack of commercialized tools. This paper provides an overview of the concept and major challenges of smart fault detection and diagnosis, evaluates recent methods, and discusses future opportunities and perspectives.
Process fault detection and diagnosis (FDD) is an essential tool to ensure safe production in chemical industries. After decades of development, despite the promising performance of some FDD methods on specific tasks, most FDD methods are not smart enough to tackle the complex challenges in real industrial processes, rendering an absence of commercialized FDD tools. Therefore, the implementation of smart FDD becomes an ambitious goal for process safety. In this paper, we provide an overview of the concept and major challenges of smart FDD. Recent FDD methods are comprehensively evaluated with respect to the characteristics of smart FDD. We also present the researches done by our group, which we believe would be a step forward for smart FDD. A range of future opportunities and new perspectives are further discussed. This review aims to illuminate potential directions for process safety and to contribute to the realization of commercial FDD tools. (C) 2022 Elsevier Ltd. All rights reserved.
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