Explainable fault detection and diagnosis based on an IDEOA: application to an industrial process
1
LGEM, Electromechanical Engineering Laboratory, Electromechanics Department, Faculty of Technology, Badji Mokhtar-Annaba University, B.P. 12, Annaba, 23000, Algeria
2
ICOSI Lab, Mathematics and Computer Science Department, Faculty of Science and Technology, Abbas Laghrour University, Khenchela, 40000, Algeria
3
Automation and Manufacturing Engineering Laboratory, Industrial Engineering Department, Faculty of Technology, Batna2 University, Batna, 05000, Algeria
Submission date: 2024-05-25
Final revision date: 2025-02-12
Acceptance date: 2025-03-22
Online publication date: 2025-03-25
Publication date: 2025-03-25
Corresponding author
Benbrahim Meriem
LGEM, Electromechanical Engineering Laboratory, Electromechanics Department, Faculty of Technology, Badji Mokhtar-Annaba University, B.P. 12, Annaba, 23000, Algeria
LGEM, Electromechanical Engineering Laboratory, Electromechanics Department, Faculty of Technology, Badji Mokhtar-Annaba University, B.P. 12, Annaba, 23000, Algeria
KEYWORDS
Fault detectionrule-based classificationImproved Discrete Equilibrium Optimizer AlgorithmInterpretable rules
TOPICS
ABSTRACT
Even with all measures approved by industrial sector specialists to avoid faults, this field still suffers from some issues. Therefore, the safety and reliability of these industrial systems become necessary, leading to focus more on anticipating fault occurrence by giving FDD a high priority. To solve this problem, a large set of reliable methods has been developed. ML-based methods have gained significant importance as they have achieved promising results. However, explainable models aim to show features that influence the detection model decision. In this study, an IDEOA was proposed to generate a rule-based fault detection model easily explainable by reading its classification rules. To this end, the OBL strategy is adopted in the IDEOA to avoid being stuck in local optima. A key contribution of this study is the novel application of the methodology to the TEP. The result of this study is a fault diagnosis model that consists of 16 rules, six of them belong to normal operating conditions and the rest reveal fault occurrence (F4). Then, an accuracy value is calculated to assess the effectiveness of our approach by contrasting it with other algorithms described in the literature. The findings indicate that the proposed approach outperforms other methods.
FUNDING
This research received no external funding.
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