Convolutional Kernel-Based Transformation and Clustering of Similar Industrial Alarm Floods
Gianluca Manca, Marcel Dix, Alexander Fay · 2022
Alarm flood similarity analysis (AFSA) methods group similar historical alarm floods and serve as a preprocessing step for further analysis. The discovered alarm flood clusters can be used for online classification to alert the operator if a similar situation recurs. In state-of-the-art AFSA methods, however, similarity measures are calculated based on only a small set of dynamic properties. As a result, more complex dynamic similarities between alarm floods are left out. To address and solve this limitation, a novel machine learning-based AFSA method is presented in this paper that uses alarm series as input to a recently proposed multivariate time series transformation method called “minimally random convolutional kernel transform with multiple pooling operators and transformations” (MultiRocket). This method is used to extract a variety of features and considers the relationships between different alarms, their dynamic properties, and the global structure of an alarm flood to a greater extent. Using an openly accessible dataset based on the simulated “Tennessee-Eastman” process, our method is compared with four relevant methods from the literature. Our results show that placing a greater emphasis on dynamics and structures improves the proposed AFSA’s overall performance and robustness in cases where higher-order alarm flood similarities are of interest.