Explainable AI for industrial alarm flood classification using counterfactuals
Publication date
2023-11-16
Document type
Konferenzbeitrag
Author
Manca, Gianluca
Organisational unit
Conference
49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023) ; Singapore, Singapore ; October 16–19, 2023
Publisher
IEEE
Book title
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Part of the university bibliography
✅
Language
English
Abstract
In this paper, we propose a novel method for enhancing the explainability of alarm flood classification results using and adapting concepts from the field of explainable artificial intelligence. Alarm flood classification methods are helpful in managing complex industrial processes; however, their predictions can be challenging to understand and justify, especially for operators without expertise in machine learning. Our proposed model-agnostic method generates counterfactual alarm floods to provide explanations for classification results obtained from any alarm flood classification model. By examining the differences between the original alarm flood and counterfactuals, we provide actionable insights for plant operators for decision-making and understanding the underlying dynamics of alarm floods. We demonstrate the effectiveness of our approach by experiments on three state-of-the-art alarm floods classification methods and an openly accessible dataset based on the "Tennes-see-Eastman" process, showcasing the added value of our method in improving the explainability and trustworthiness of alarm flood classification results.
Version
Published version
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