DOI: 10.57760/sciencedb.33801
title: 250934Enhance publishing丨Unsupervised Anomaly Detection of Hydro-Turbine Generator Acoustics by Integrating Pre-Trained Audio Large Model and Density Estimation
title: 250934增强出版丨融合预训练音频大模型与密度估计的水轮发电机组声学无监督异常检测

description: Declaration: If this open-source content is used in papers, books, academic reports, or other works, please cite the following publication (refer to the original link for the latest citation format):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/JEIT250934DOI: 10.11999/JEIT250934Original Article: [Unsupervised Acoustic Anomaly Detection for Hydro-turbine Generator Units Fusing Pre-trained Large Audio Models and Density Estimation]Corresponding Author: WU Ting, wuting@mail.ioa.ac.cnOpen Source Date: December 03, 2025Funding: China Yangtze Power Co., Ltd. Project (Z152302048)Open Source ContentUnsupervised Acoustic Anomaly Detection for Hydro-turbine Generator Units Fusing Pre-trained Large Audio Models and Density Estimation - Reproduction CodeAs the core power equipment of hydropower stations, the safe and stable operation of hydro-turbine generator units is of great significance to the entire station. In recent years, non-contact acoustic measurement has received widespread attention as an effective detection method. However, it is difficult to collect abnormal acoustic signals during the actual operation of hydro-turbine generator units, which restricts the application of traditional anomaly detection methods and supervised learning-based classification strategies in this field. To address these challenges, this paper proposes an unsupervised acoustic anomaly detection method for hydro-turbine generators that fuses pre-trained large audio models with density estimation k-nearest neighbors (k-NN). First, the validity of general audio features extracted by pre-trained audio models in anomaly detection is verified. Subsequently, a parameter fine-tuning strategy integrating attention statistical pooling and warm-up is designed to achieve model transfer optimization. In the inference stage, a density estimation k-NN is designed to realize a robust distance metric. Experimental results show that the proposed method achieves a multi-metric harmonic mean of 98.7% in the wind tunnel environment and up to 99.9% in the slip ring chamber, providing a feasible and excellent solution for acoustic anomaly detection in hydropower stations.
description: Declaration: If this open-source content is used in papers, books, academic reports, or other works, please cite the following publication (refer to the original link for the latest citation format):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/JEIT250934DOI: 10.11999/JEIT250934Original Article: [Unsupervised Acoustic Anomaly Detection for Hydro-turbine Generator Units Fusing Pre-trained Large Audio Models and Density Estimation]Corresponding Author: WU Ting, wuting@mail.ioa.ac.cnOpen Source Date: December 03, 2025Funding: China Yangtze Power Co., Ltd. Project (Z152302048)Open Source ContentUnsupervised Acoustic Anomaly Detection for Hydro-turbine Generator Units Fusing Pre-trained Large Audio Models and Density Estimation - Reproduction CodeAs the core power equipment of hydropower stations, the safe and stable operation of hydro-turbine generator units is of great significance to the entire station. In recent years, non-contact acoustic measurement has received widespread attention as an effective detection method. However, it is difficult to collect abnormal acoustic signals during the actual operation of hydro-turbine generator units, which restricts the application of traditional anomaly detection methods and supervised learning-based classification strategies in this field. To address these challenges, this paper proposes an unsupervised acoustic anomaly detection method for hydro-turbine generators that fuses pre-trained large audio models with density estimation k-nearest neighbors (k-NN). First, the validity of general audio features extracted by pre-trained audio models in anomaly detection is verified. Subsequently, a parameter fine-tuning strategy integrating attention statistical pooling and warm-up is designed to achieve model transfer optimization. In the inference stage, a density estimation k-NN is designed to realize a robust distance metric. Experimental results show that the proposed method achieves a multi-metric harmonic mean of 98.7% in the wind tunnel environment and up to 99.9% in the slip ring chamber, providing a feasible and excellent solution for acoustic anomaly detection in hydropower stations.