Alarm systems are critically important for safe and efficient operations of industrial plants, but many industrial alarm systems are suffering from too many nuisance alarms. This paper proposes a method to classify normal and abnormal data segments and evaluate performance indices for the most commonly used univariate alarm systems. The proposed method consists of three steps. First, piece-wise linear representations are exploited in separating historical data samples of an analog process variable configured with alarms into data segments with same qualitative trends. Second, data segments are classified into normal, abnormal and unclassified conditions via a mean hypothesis test; a required assumption is that data segments in normal and abnormal conditions have different mean values being distinguishable from alarm thresholds. Third, based on the normal and abnormal data, performance indices of univariate alarm systems are calculated, including two newly formulated ones as the false alarm duration ratio and the missed alarm duration ratio. The effectiveness of the proposed method is illustrated by numerical and industrial examples.

警报系统对于工厂的安全有效运行至关重要，但是许多工业警报系统都遭受过多的有害警报。本文提出了一种对正常和异常数据段进行分类并评估最常用单变量警报系统性能指标的方法。所提出的方法包括三个步骤。首先，利用分段线性表示法将配置了警报的模拟过程变量的历史数据样本分离为具有相同定性趋势的数据段。其次，通过均值假设检验将数据段分为正常，异常和未分类条件。一个必需的假设是，正常和异常条件下的数据段均具有与警报阈值可区分的不同平均值。第三，根据正常和异常数据，计算出单变量报警系统的性能指标，包括两个新制定的误报时长比和漏报时长比。数值和工业实例说明了该方法的有效性。