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

Srijesh Pillai, Yodhin Agarwal, Zaheeruddin Ahmed
9/14/2025
cs.LG

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Abstract

Audio-based equipment condition monitoring suffers from a lack of standardized methodologies for algorithm selection, hindering reproducible research. This paper addresses this gap by introducing a comprehensive framework for the systematic and statistically rigorous evaluation of machine learning models. 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%. Ultimately, this work provides a validated benchmarking protocol and practical guidelines for selecting robust monitoring solutions in industrial settings.

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Cite this paper

@article{pillai2025machine,
  title  = {Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms},
  author = {Srijesh Pillai and Yodhin Agarwal and Zaheeruddin Ahmed},
  year   = {2025},
  eprint = {2509.11075},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.11075v1}
}

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