Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms

Personal use of this material is permitted. This work has been accepted for publication in the proceedings of the 2025 Advances in Science and Engineering Technology International Conferences (ASET). Zaheeruddin Ahmed Department of Computer Science & Engineering Manipal Academy of Higher Education Dubai, UAE zaheeruddin@manipaldubai.com Abstract -- Audio - based equipment condition monitoring suffers from a lack of standardized methodologies for algorithm selection, hindering reproducible research. Leveraging a rich 127 - feature set across time, frequency, and time - frequency domains, our methodology is validated on both synthetic and real - world datasets. Results demonstrate that an ensemble method achieves superior performance (94.2% accuracy, 0.942 F1 - score), with statistical testing confirming its significant outperformance of individual algorithms by 8 - 15%.