Alarm systems are of vital importance in the safe and effective functioning of industrial plants, yet they frequently suffer from too many nuisance alarms (alarm overloading). It is necessary to intelligently enhance existing alarm systems and supply accurate information for the operators. Nowadays, process variables are more correlated and complicated. This correlation structure can be used as a basis to manage alarms efficiently. Hence, multivariate approaches are more appropriate. Designing a system aimed at reducing nuisance alarms is an essential phase to guarantee the reliable operation of a plant. Due to the definition of alarm limits, the problem of false alarms is inevitable in multivariate methods. In this paper, the conventional Principal Component Analysis (PCA) is applied to extract the sum of squared prediction error (SPE) known as the $Q$ statistic and the Hotelling $T^2$ statistic. These statistics are used separately as alarm indicators where their control limits are duly modified. Consequently, for each statistic, a nonlinear combination of alarm duration and alarm deviation, is additionally exploited as a new requirement to activate an alarm or not. The resulting new index is fed to a delay timer with a defined parameter $n$. The implementation of this technique resulted in a significant reduction in the severity of alarm overloading. Historical data collected from the cement rotary kiln operating under healthy conditions are employed to adequately build the PCA model and extract the proposed alarming indexes. Then, various testing data sets, covering different types of faults occurring in the cement process, are used to assess the performance of the developed method. In comparison with the conventional PCA technique, alarms are better managed nd almost nuisance alarms are suppressed. The proposed method is more robust to false alarms and more sensitive to fault detection.

警报系统对于工厂的安全有效运行至关重要，但是它们经常遭受过多的令人讨厌的警报（警报过载）。有必要智能地增强现有的警报系统并为操作员提供准确的信息。如今，过程变量之间的相关性和复杂性越来越高。该相关结构可以用作有效管理警报的基础。因此，多元方法更为合适。设计旨在减少骚扰警报的系统是确保工厂可靠运行的必不可少的阶段。由于警报限制的定义，在多变量方法中不可避免地会出现虚假警报的问题。在本文中，$问$ 统计与霍特林 ${Ť}^{\mathrm{2个}}$统计。这些统计信息分别用作警报指示器，在这些指示器中它们的控制限制已得到适当修改。因此，对于每个统计数据，警报持续时间和警报偏差的非线性组合也被额外用作激活警报或不激活警报的新要求。产生的新索引将被馈送到具有已定义参数的延迟计时器$ñ$。此技术的实施大大降低了警报过载的严重性。从在健康条件下运行的水泥回转窑收集的历史数据可用于充分构建PCA模型并提取建议的警报指标。然后，使用覆盖水泥过程中发生的不同类型故障的各种测试数据集来评估所开发方法的性能。与传统的PCA技术相比，可以更好地管理警报，并且可以抑制几乎令人讨厌的警报。所提出的方法对虚假警报更鲁棒，对故障检测更敏感。