Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models.

警报洪水是现代化工厂普遍存在的问题。在这些情况下，警报的数量可能无法控制，并且操作员可能会错过对安全至关重要的警报。震颤警报在活动状态和非活动状态之间反复变化，是洪水事件中大部分警报记录的原因。通常，颤振警报只能追溯解决（例如在定期性能评估期间）。这项研究提出了一种基于机器学习的方法来进行警报颤振预测。具体来说，已开发出一种用于动态颤动量化的方法，其结果已用于训练三种不同的机器学习模型-线性，深度和宽与深度模型。该算法已被用于根据实际工厂状况预测未来的抖振行为。