Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation

WU Ting; WEN Shulin; YAN Zhaoli; FU Gaoyuan; LI Linfeng; LIU Xudu; CHENG Xiaobin; YANG Jun

doi:10.11999/JEIT250934

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WU Ting, WEN Shulin, YAN Zhaoli, FU Gaoyuan, LI Linfeng, LIU Xudu, CHENG Xiaobin, YANG Jun. Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250934

Citation:

WU Ting, WEN Shulin, YAN Zhaoli, FU Gaoyuan, LI Linfeng, LIU Xudu, CHENG Xiaobin, YANG Jun. Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250934

Citation:

WU Ting, WEN Shulin, YAN Zhaoli, FU Gaoyuan, LI Linfeng, LIU Xudu, CHENG Xiaobin, YANG Jun. Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250934

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Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation

doi: 10.11999/JEIT250934 cstr: 32379.14.JEIT250934

WU Ting^{1, 2, 4},
WEN Shulin^{3, 4},
YAN Zhaoli^{5, 1},
FU Gaoyuan^{3, 4},
LI Linfeng^{3, 4},
LIU Xudu^{3, 4},
CHENG Xiaobin^{1, 2
,
,},
YANG Jun^{1, 2}

1.
State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing 100049, China
3.
China Yangtze Power Co., Ltd, Wuhan 443000, China
4.
Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430000, China
5.
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China

Funds: China Yangtze Power Co., Ltd. - Research and demonstration application of acoustic monitoring system for river basin power station equipment status based on industrial Internet platform (Z152302048)

Accepted Date: 2025-11-12
Rev Recd Date: 2025-11-12

Available Online: 2025-11-18

Abstract

Abstract

Objective Hydro-turbine generator units (HTGUs) require reliable early-stage fault detection to ensure operational safety and reduce maintenance costs. Acoustic signals provide a non-intrusive and sensitive monitoring modality, yet their use is hindered by complex structural acoustics, strong background noise, and the scarcity of abnormal data. This work presents an unsupervised acoustic anomaly detection framework that integrates a large-scale pretrained audio model with density-based k-nearest neighbors estimation, enabling accurate anomaly detection using only normal data while maintaining robustness and strong generalization across diverse HTGU conditions. Methods The proposed framework performs unsupervised acoustic anomaly detection for HTGUs using only normal data. Time-domain signals are preprocessed with Z-score normalization and Fbank features, followed by random masking to enhance robustness and generalization. A large-scale pretrained BEATs model serves as the feature encoder, and an Attentive Statistical Pooling module is applied to aggregate frame-level representations into discriminative segment-level embeddings by emphasizing informative frames. To improve class separability, an ArcFace loss replaces the conventional classification layer during training. A warm-up learning rate strategy is adopted to ensure stable convergence. During inference, density-based k-nearest neighbors estimation is performed on the learned embeddings to detect acoustic anomalies. Results and Discussions This study verifies the effectiveness of the proposed unsupervised acoustic anomaly detection framework for HTGUs using data collected from eight real-world machines. As shown in Fig. 7 and Table 2, large-scale pretrained audio representations significantly outperform traditional features in distinguishing abnormal sounds. With the FED-KE algorithm, the method achieves high accuracy across six metrics, with Hmean reaching 98.7% in the wind tunnel and over 99.9% in the slip-ring environment, demonstrating strong robustness under complex industrial conditions. As shown in Table 4, ablation studies confirm the complementary contributions of feature enhancement, ASP-based representation refinement, and density-based k-NN inference. The framework requires only normal data for training, reducing dependence on scarce fault labels and improving practical applicability. Remaining challenges include computational cost due to the pretrained model and the lack of multimodal fusion, which will be investigated in future work. Conclusions This study proposes an unsupervised acoustic anomaly detection framework for HTGUs, addressing the scarcity of fault samples and the complexity of industrial acoustic environments. A pretrained large-scale audio foundation model is adopted and further fine-tuned with turbine-specific strategies to enhance the modeling of normal operational acoustics. During inference, a density-estimation-based k-NN mechanism is employed to detect abnormal patterns using only normal data. Experiments on real-world hydropower station recordings demonstrate high detection accuracy and strong generalization across diverse operating conditions, outperforming conventional supervised approaches. The framework introduces foundation-model-based audio representation learning into the hydro-turbine domain, establishes an efficient adaptation strategy tailored to turbine acoustics, and integrates a robust density-based anomaly scoring mechanism. These components jointly reduce reliance on labeled anomalies and enable practical deployment for intelligent condition monitoring. Future work will investigate model compression, such as knowledge distillation, to support on-device deployment, and explore semi-/self-supervised learning and multimodal fusion to further enhance robustness, scalability, and cross-station adaptability.
- Pretrained Audio Models,
- Hydropower Generating Units,
- Anomaly Detection,
- Unsupervised Deep Learning

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References(29)

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