A Bayesian belief-rule-based inference multivariate alarm system for nonlinear time-varying processes  

作　　者：Xiaobin XU Zhuochen YU Jiusun ZENG Wanqi XIONG Yanzhu HU Guodong WANG 

机构地区：[1]School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China [2]College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou 310018,China [3]College of Engineering,Peking University,Beijing 100080,China [4]School of Automation,Beijing University of Posts and Telecommunications,Beijing 100876,China [5]Institute of Computer Engineering,Vienna University of Technology,Vienna 1040,Austria

出　　处：《Science China(Information Sciences)》2021年第10期190-201,共12页中国科学（信息科学）（英文版）

基　　金：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);Research Fund of National Health Commission(Grant No.WKJ-ZJ-2038)。

摘　　要：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.

关 键 词：multivariate alarm design belief-rule-based method nonlinear time-varying process sequential Monte Carlo 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]