Explainability for Fault Detection System in Chemical Processes

• URL: http://arxiv.org/abs/2602.16341v1
• Date: Wed, 18 Feb 2026 10:26:12 GMT
• Title: Explainability for Fault Detection System in Chemical Processes
• Authors: Georgios Gravanis, Dimitrios Kyriakou, Spyros Voutetakis, Simira Papadopoulou, Konstantinos Diamantaras,
• Abstract summary: We apply and compare two state-of-the-art Artificial Intelligence (XAI) methods, that explain the fault diagnosis decisions of a highly accurate Long Short-Time Memory (LSTM)<n>It is highlighted how XAI methods can help identify the subsystem of the process where the fault occurred.<n>The proposed approach is not limited to the specific process and can also be used in similar problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we apply and compare two state-of-the-art eXplainability Artificial Intelligence (XAI) methods, the Integrated Gradients (IG) and the SHapley Additive exPlanations (SHAP), that explain the fault diagnosis decisions of a highly accurate Long Short-Time Memory (LSTM) classifier. The classifier is trained to detect faults in a benchmark non-linear chemical process, the Tennessee Eastman Process (TEP). It is highlighted how XAI methods can help identify the subsystem of the process where the fault occurred. Using our knowledge of the process, we note that in most cases the same features are indicated as the most important for the decision, while insome cases the SHAP method seems to be more informative and closer to the root cause of the fault. Finally, since the used XAI methods are model-agnostic, the proposed approach is not limited to the specific process and can also be used in similar problems.

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