Anomalous sound detection for hydroelectric plant equipment based on self-encoder and weakly supervised learning

Yumin Peng; Zengtao Zhao; Fanqi Huang; Liehao Hu

doi:10.1117/12.2681050

23 May 2023 Anomalous sound detection for hydroelectric plant equipment based on self-encoder and weakly supervised learning

Yumin Peng, Zengtao Zhao, Fanqi Huang, Liehao Hu

Author Affiliations +

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452M (2023) https://doi.org/10.1117/12.2681050
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The current traditional method of detecting abnormal sound of hydropower plant equipment identifies the equipment state by a single class of support vector machine to achieve the judgment of abnormal sound, which leads to poor detection effect due to the lack of extraction of audio features of abnormal sound. In this regard, the abnormal sound detection method for hydro power plant equipment based on self-encoder and weakly supervised learning is proposed. The Gaussian mixture model is used to learn the characteristics of equipment sound and purify the abnormal sound of hydropower plant equipment. The audio features are extracted from the abnormal sound of equipment, and a two-channel weakly supervised encoder detection model is constructed to realize the detection of abnormal sound of hydropower plant equipment. In the experiment, the detection effect of the proposed method is verified. The analysis of the experimental results shows that the anomalous sound detection model constructed by the proposed method has a high AUC value and a high detection performance.

Citation Download Citation

Yumin Peng, Zengtao Zhao, Fanqi Huang, and Liehao Hu "Anomalous sound detection for hydroelectric plant equipment based on self-encoder and weakly supervised learning", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452M (23 May 2023); https://doi.org/10.1117/12.2681050

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