对比三种声学异常检测模型，提升抽水蓄能电站预测维护能力
From Noise to Knowledge: A Comparative Study of Acoustic Anomaly Detection Models in Pumped-storage Hydropower Plants
- 针对高噪声环境优化声学预处理，提取时频域特征
- 单类SVM在准确率（AUC 0.966-0.998）和训练时间上表现最佳
- 适合关注实时性与部署效率的工业维护场景
在工业工厂与能源生产领域，非计划停机成本高昂且难以修复。然而现有声学异常检测研究多依赖通用工业或合成数据集，针对水电站的研究极少，因数据获取困难。本文对基于声学的异常检测方法进行对比分析，以提升水电站预测性维护水平。针对高噪声条件下的声学预处理难题，提出时频域特征提取方案。在奥地利罗顿德沃尔克II抽水蓄能电站的两组真实数据集上，测试了LSTM自编码器、K-Means与单类SVM三种模型。结果表明，单类SVM在准确率（ROC AUC 0.966–0.998）与训练时间间取得最佳平衡；LSTM自编码器虽检测性能较强（ROC AUC 0.889–0.997），但计算开销更高。
原文摘要 · Abstract (English)
In the context of industrial factories and energy producers, unplanned outages are highly costly and difficult to service. However, existing acoustic-anomaly detection studies largely rely on generic industrial or synthetic datasets, with few focused on hydropower plants due to limited access. This paper presents a comparative analysis of acoustic-based anomaly detection methods, as a way to improve predictive maintenance in hydropower plants. We address key challenges in the acoustic preprocessing under highly noisy conditions before extracting time- and frequency-domain features. Then, we benchmark three machine learning models: LSTM AE, K-Means, and OC-SVM, which are tested on two real-world datasets from the Rodundwerk II pumped-storage plant in Austria, one with induced anomalies and one with real-world conditions. The One-Class SVM achieved the best trade-off of accuracy (ROC AUC 0.966-0.998) and minimal training time, while the LSTM autoencoder delivered strong detection (ROC AUC 0.889-0.997) at the expense of higher computational cost.
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