Loading [a11y]/accessibility-menu.js
An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers | IEEE Conference Publication | IEEE Xplore

An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers


Abstract:

Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practica...Show More

Abstract:

Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model training, resulting in limited diagnostic performance. To address this problem, we propose an abnormal audio generation method based on the improved cycle generative adversarial networks (ImCycleGAN) to augment the limited training dataset. In the ImCycleGAN, the generator and discriminator are redesigned and adversarially trained to generate the realistic-like audios of six abnormal statuses. Moreover, we combine adversarial, cycle consistency and identity mapping losses to optimize the training process of ImCycleGAN and enhance its ability to capture nonlinear features of audios. Finally, the generated data is evaluated in terms of similarity and fault classification. Experimental results show that our method can generate abnormal audios with high similarity to the real ones, and significantly improve the classification accuracy of existing fault diagnosis models.
Date of Conference: 06-11 April 2025
Date Added to IEEE Xplore: 07 March 2025
ISBN Information:

ISSN Information:

Conference Location: Hyderabad, India

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.