Accurate diagnosis of mechanical faults in a single-phase AC electromotor through acoustic monitoring and machine learning techniques

Samadi, V.; Mostafaei, M.; Lorestani, A. N.

doi:10.22061/jcarme.2026.12187.2662

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	Accurate diagnosis of mechanical faults in a single-phase AC electromotor through acoustic monitoring and machine learning techniques
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 دی 1404 اصل مقاله (935.23 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2026.12187.2662
نویسندگان
V. Samadi¹؛ M. Mostafaei^* ¹^{، 2}؛ A. N. Lorestani¹
¹Department of Mechanical Engineering of Biosystems, Razi University, Kermanshah, Iran
²Department of Biosystems Engineering, University of Tabriz, Tabriz, Iran
تاریخ دریافت: 08 تیر 1404، تاریخ بازنگری: 16 دی 1404، تاریخ پذیرش: 20 دی 1404
چکیده
This study presents a non-invasive method for detecting mechanical faults in a single-phase AC electromotor using processed acoustic signals. Sound data were collected via a USB-connected microphone installed in the motor's electrical casing under diverse operating conditions. Ten statistical features were extracted from the acoustic signals and used as input to three classification algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). Model performance was evaluated using confusion matrix metrics, including specificity, accuracy, precision, and sensitivity. Among the classifiers, SVM outperformed others, achieving average values of 99.54, 99.16, 97.15, and 96.17, respectively, with 10-fold cross-validation confirming its superior consistency (99.88% specificity, 99.17% accuracy). The findings confirm that acoustic signal analysis is a reliable and cost-effective tool for real-time fault diagnosis in electromotors. Defects may be accurately found in the electromotor by using acoustic analysis to monitor its status. The proposed framework is adaptable to other rotating machinery through retraining, offering a valuable solution for predictive maintenance in industrial applications.
کلیدواژه‌ها
Acoustic signals؛ Classification؛ Electromotor؛ Fault detection؛ Machine learning

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Accurate diagnosis of mechanical faults in a single-phase AC electromotor through acoustic monitoring and machine learning techniques