Convolutional kernel-based classification of industrial alarm floods

Alarm flood classification (AFC) methods are crucial in assisting human operators to identify and mitigate the overwhelming occurrences of alarm floods in industrial process plants, a challenge exacerbated by the complexity and data-intensive nature of modern process control systems. These alarm floods can significantly impair situational awareness and hinder decision-making. Existing AFC methods face difficulties in dealing with the inherent ambiguity in alarm sequences and the task of identifying novel, previously unobserved alarm floods. As a result, they often fail to accurately classify alarm floods. Addressing these significant limitations, this paper introduces a novel three-tier AFC method that uses alarm time series as input. In the transformation stage, alarm floods are subjected to an ensemble of convolutional kernel-based transformations (MultiRocket) to extract their characteristic dynamic properties, which are then fed into the classification stage, where a linear ridge regression classifier ensemble is used to identify recurring alarm floods. In the final novelty detection stage, the local outlier probability (LoOP) is used to determine a confidence measure of whether the classified alarm flood truly belongs to a known or previously unobserved class. Our method has been thoroughly validated using a publicly available dataset based on the Tennessee-Eastman process. The results show that our method outperforms two naive baselines and four existing AFC methods from the literature in terms of overall classification performance as well as the ability to optimize the balance between accurately identifying alarm floods from known classes and detecting alarm flood classes that have not been observed before. Impact Statement We introduce the convolutional kernel-based alarm subsequence identification method (CASIM), which improves industrial alarm flood classification. CASIM extracts a wide range of alarm dynamics, unlike previous approaches that use a limited set of alarm characteristics. This helps CASIM identify more relevant features, improving its ability to classify complex alarm floods. Moreover, expanding windows in CASIM's online application, inspired by early time series classification, allows alarm flood classification over time. Our evaluation shows that this can provide faster and more accurate insights than existing methods. We believe that our proposed method CASIM can improve operational decision-making and reduce operator effort. By making the implementation of our method publicly available, we aim to encourage wider adoption and research in the field.