Paper Info
Title
Convolutional Kernel-Based Transformation and Clustering of Similar Industrial Alarm Floods
Abstract
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.
Year
DOI
Venue
2022
10.1109/BigDataService55688.2022.00033
2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService)
Keywords
DocType
ISBN
Abnormal plant situations,fault detection and diagnosis,industrial alarm floods,time series transformation
Conference
978-1-6654-5891-7
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Gianluca Manca100.34
Marcel Dix200.68
Alexander Fay344.14