期刊
CONTROL ENGINEERING PRACTICE
卷 110, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2021.104778
关键词
Variational Bayesian inference; Distributed process monitoring; Fault detection; Latent variable model; Probabilistic modeling
资金
- National Natural Science Foundation of China [61973119]
- Shanghai RisingStar Program, China [20QA1402600]
This study proposes a probabilistic modeling approach based on variational Bayesian for distributed process monitoring, characterizing the variable relationships within and among local units through latent variable models, and verifies the effectiveness of the method through three case studies.
Data-driven process monitoring has gained increasing attention because of the increasing demand in process safety and the rapid advancement of data gathering techniques. When monitoring a plant-wide multiunit process, establishing a monitor for each unit individually ignores the correlations among units, whereas establishing a global monitor for the entire process ignores the local process behavior. A variational Bayesian-based probabilistic modeling approach is proposed for efficient distributed process monitoring. A novel probabilistic latent variable model is developed to characterize the variable relationship in each local unit and among units. First, variational Bayesian-based latent variable extraction is performed in each local unit, through which variable relationship within a local unit is characterized. Second, variational Bayesian-based regression model is established between the latent variables and neighboring variables, through which the variable relationship among units is characterized. Then, modeling residuals and monitoring statistics are generated, through which the process status and the type of a detected fault are identified. The effectiveness of the proposed probabilistic modeling and monitoring method is verified by three case studies, including a numerical example, the Tennessee Eastman benchmark process, and a laboratory distillation process.
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