Abstract
This study considers the multivariate alarm design problem of nonlinear time-varying systems by a Bayesian belief-rule-based (BRB) method. In the method, the series of belief rules are constructed to approximate the relationship between input and output variables. Hence, the method does not require an explicit model structure and is suitable for capturing nonlinear causal relationships between variables. For the purpose of online application, this study further introduces sequential Monte Carlo (SMC) sampling to update the BRB model parameters, which is a fast and efficient method for approximately inferring nonlinear sequence models. Using the model parameters obtained by SMC sampling, the series of output variable tracking errors can be estimated and employed for multivariate alarm design. The case study of a condensate pump verifies the effectiveness of the proposed method.
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Acknowledgements
This work was supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization, China (Grant No. U1709215), National Natural Science Foundation of China (Grant No. 61673358), Zhejiang Province Key R&D Projects (Grant Nos. 2019C03104, 2018C04020), Zhejiang Province Public Welfare Technology Application Research Project (Grant No. LGF20H270004), and Research Fund of National Health Commission (Grant No. WKJ-ZJ-2038).
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Xu, X., Yu, Z., Zeng, J. et al. A Bayesian belief-rule-based inference multivariate alarm system for nonlinear time-varying processes. Sci. China Inf. Sci. 64, 202203 (2021). https://doi.org/10.1007/s11432-020-3029-6
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DOI: https://doi.org/10.1007/s11432-020-3029-6