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.

本研究通过基于贝叶斯置信规则 (BRB) 的方法考虑非线性时变系统的多元警报设计问题。在该方法中，构建了一系列置信规则来近似输入和输出变量之间的关系。因此，该方法不需要明确的模型结构，适用于捕捉变量之间的非线性因果关系。出于在线应用的目的，本研究进一步引入了顺序蒙特卡罗（SMC）采样来更新BRB模型参数，这是一种快速有效的近似推断非线性序列模型的方法。使用SMC采样获得的模型参数，可以估计输出变量跟踪误差序列，并将其用于多变量报警设计。