Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms
This work provides a validated benchmarking protocol and practical guidelines for selecting robust monitoring solutions in industrial settings, addressing a domain-specific problem with incremental improvements.
The paper tackled the lack of standardized methodologies for algorithm selection in audio-based equipment condition monitoring by introducing a comprehensive evaluation framework, resulting in an ensemble method achieving 94.2% accuracy and significantly outperforming individual algorithms by 8-15%.
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.