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 flo...
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Cambridge University Press
2024-01-01
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| Series: | Data-Centric Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673624000224/type/journal_article |
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| author | Gianluca Manca Alexander Fay |
| author_facet | Gianluca Manca Alexander Fay |
| author_sort | Gianluca Manca |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ad4d8890c488451dac59136fd92ef46f |
| institution | OA Journals |
| issn | 2632-6736 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj-art-ad4d8890c488451dac59136fd92ef46f2025-08-20T02:05:14ZengCambridge University PressData-Centric Engineering2632-67362024-01-01510.1017/dce.2024.22Convolutional kernel-based classification of industrial alarm floodsGianluca Manca0https://orcid.org/0000-0001-5951-8590Alexander Fay1Institute of Automation Technology, Helmut-Schmidt-University Hamburg, Hamburg, Germany Industrial AI, ABB Corporate Research Center, Ladenburg, GermanyInstitute of Automation Technology, Helmut-Schmidt-University Hamburg, Hamburg, GermanyAlarm 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.https://www.cambridge.org/core/product/identifier/S2632673624000224/type/journal_articleabnormal situationsindustrial alarm floodsindustrial process diagnosisopen-set classificationtime series classification |
| spellingShingle | Gianluca Manca Alexander Fay Convolutional kernel-based classification of industrial alarm floods Data-Centric Engineering abnormal situations industrial alarm floods industrial process diagnosis open-set classification time series classification |
| title | Convolutional kernel-based classification of industrial alarm floods |
| title_full | Convolutional kernel-based classification of industrial alarm floods |
| title_fullStr | Convolutional kernel-based classification of industrial alarm floods |
| title_full_unstemmed | Convolutional kernel-based classification of industrial alarm floods |
| title_short | Convolutional kernel-based classification of industrial alarm floods |
| title_sort | convolutional kernel based classification of industrial alarm floods |
| topic | abnormal situations industrial alarm floods industrial process diagnosis open-set classification time series classification |
| url | https://www.cambridge.org/core/product/identifier/S2632673624000224/type/journal_article |
| work_keys_str_mv | AT gianlucamanca convolutionalkernelbasedclassificationofindustrialalarmfloods AT alexanderfay convolutionalkernelbasedclassificationofindustrialalarmfloods |