From Noise to Knowledge: A Comparative Study of Acoustic Anomaly Detection Models in Pumped-storage Hydropower Plants
Type
conference paper
Date Issued
2025
Author(s)
;
Keller, Nicolas
;
Stefan Krummenacher
;
Valentin Huber
;
Fäßler, Bernhard
;
Abstract
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.
Language
English (United States)
Event Title
The 15th International Conference on the Internet of Things
Event Location
Vienna
Official URL
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Name
ACM-IoT-NoiseToKnowledge.pdf
Size
8.63 MB
Format
Adobe PDF
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