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Machine Fault Dia.n.sis Using Audio Sensors Data and Expla.n.ble AI Techniques-LIME and SHAP

Computers, Materials and Continua 2024年第3期80卷 3463-3484页

作者： zereen a.n. Das A. Uddin J. School of Data and Sciences Brac University Dhaka 1212 Bangladesh JW KIM College of Future Studies Endicott College Woosong University Daejeon 300-718 South Korea AI and Big Data Department Endicott College Woosong University Daejeon 300-718 South Korea

Machine fault dia.n.stics are essential for industrial operations, and advancements in machine lea.n.ng have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine lea.n.ng models, especially those utilizing complex algorithms like deep lea.n.ng, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine lea.n.ng models face challenges, making Expla.n.ble AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the dia.n.sis of machine faults is examined in this study. The technique is applied to evaluate different machine-lea.n.ng algorithms. Extreme Gradient Boosting, Support Vector Machine, Gaussian naive Bayes, and Random Forest classifiers are used alongside Logistic Regression (LR) as a baseline model because their efficacy and simplicity are evaluated thoroughly with empirical analysis. The XAI is used as a targeted feature selection technique to select among 29 features of the time and frequency doma.n. The XAI approach is lightweight, tra.n.d with only targeted features, and achieved similar results as the traditional approach. The accuracy without XAI on baseline LR is 79.57%, whereas the approach with XAI on LR is 80.28%. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： Expla.n.ble AI feature selection machine fault dia.n.sis machine lea.n.ng

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