提出一套音频设备监测的机器学习评估框架，助力工业场景选型。
Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms
- 构建127维时频特征集，系统评估多种分类算法性能。
- 集成方法达94.2%准确率，显著优于单个模型8-15%。
- 提供可复现的基准协议，适合工业故障监测应用。
基于音频的设备状态监测缺乏标准化的算法选择方法，阻碍了可复现研究。本文提出一个全面的框架，用于系统且统计严谨地评估机器学习模型。利用跨时域、频域和时频域的127个特征集，该方法在合成数据与真实数据集上均得到验证。结果表明，集成方法表现最优（准确率94.2%，F1分数0.942），统计检验确认其显著优于单个算法8%-15%。本研究提供了经验证的基准测试协议与实用指南，适用于工业环境中的稳健监测方案选择。
原文摘要 · Abstract (English)
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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