Alarm systems are critically important for safety and efficiency of industrial plants. However, many alarm variables in contemporary alarm systems are generated in a way being isolated from related process variables, resulting in false and missing alarms. This paper is motivated by abnormality detection for condensate-water electrical pumps in thermal power plants and proposes a method to design multivariate alarm systems for time-varying processes. A novel feature to distinguish normal and abnormal conditions is observed on the variation rates of multiple linear regression model parameters. A model estimator based on Bayesian filters is formulated to track the variations of model parameters in normal conditions, and not to do so in abnormal conditions so that absolute cumulative modeling errors are large enough to raise alarms. The effectiveness of the proposed method is validated by industrial case studies.

中文翻译：

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使用贝叶斯滤波器的时变过程的多元报警系统及其在电动泵上的应用

警报系统对于工厂的安全性和效率至关重要。但是，现代警报系统中的许多警报变量都是与相关的过程变量隔离生成的，从而导致错误和丢失的警报。本文以火力发电厂凝结水电泵的异常检测为动力，提出了一种设计用于时变过程的多元报警系统的方法。在多个线性回归模型参数的变化率上观察到了区分正常和异常情况的新功能。制定了基于贝叶斯滤波器的模型估计器，以跟踪正常条件下的模型参数变化，而不是在异常条件下跟踪模型参数的变化，因此绝对累积建模误差足够大，可以发出警报。