Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorKhan, Shaiq Ahmad
dc.contributor.authorAhmad Khan, Faiq
dc.contributor.authorJamil, Akhtar
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2025-04-18T10:26:08Z
dc.date.available2025-04-18T10:26:08Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn industries, machines communicate through sounds, decoded by predictive maintenance to prevent issues. Understanding motor sounds is crucial for seamless industrial operations. This research undertakes a comprehensive explo-ration of machine learning models, specifically Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, applied to motor sound data for classifying instances as either healthy or faulty. The ANN, boasting an 81.22 % accuracy, reveals commendable precision and recall values for both classes, indicating its robust predictive capabilities. However, there is room for improvement, particu-larly in accurately classifying healthy motors. SVM marginally outperforms the ANN with an accuracy of 81.32%, showcasing balanced precision and recall for both classes. Notably, KNN, while exhibiting a slightly lower accuracy of 80.22 %, excels in recall for the healthy class, emphasizing its efficacy in correctly identifying healthy motor sounds. Random Forest attains an accuracy of 81.32 %, featuring notably high recall for the healthy class (0.91), underscoring its proficiency in capturing instances of healthy motor sounds. In-depth metrics provide nuanced insights into the strengths and specificities of each model, offering a foundation for informed decisions based on application priorities and requirements. The study contributes not only quantitative metrics but also interpretability tools, including LIME and SHAP, to enhance transparency and elucidate the intricate patterns within motor sound data. © 2024 IEEE.
dc.identifier.citationKhan, S. A., Khan, F. A., Jamil, A., & Hameed, A. A. (2024, April). Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI. In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-10). IEEE.
dc.identifier.doi10.1109/ICMI60790.2024.10585829
dc.identifier.isbn979-835037297-7
dc.identifier.scopus2-s2.0-85199474108
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7066
dc.identifier.wosWOS:001282083300048
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFaults Diagnosis
dc.subjectGear Motors
dc.subjectMachine Learning
dc.subjectMotor Sounds
dc.titleInterpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI
dc.typeArticle

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