Work

An Attention‐Guided Semi‐Supervised Model for Power Transformer Fault Diagnosis via Vibration‐Acoustic Data Fusion

Year: 2025

Type: article

Abstract: ABSTRACT Reliable fault diagnosis of power transformers is vital for ensuring the safe and continuous operation of power systems. Although deep learning methods have shown success with single‐sensor data, their diagnostic performance remains limited due to the inability to capture complex, multimodal fault characteristics. To address this, we propose an attention‐guided semi‐supervised vibration‐acoustic fusion (AG‐SVAF) model, which combines vibration and acoustic signals to enhance diagnostic robustness under limited labelled data conditions. The model integrates time‐frequency representations derived via short‐time Fourier transform (STFT) with a multilevel attention mechanism—including channel, spatial and self‐attention—to highlight fault‐relevant features and model cross‐modal dependencies. A novel attention‐weighted consistency loss further improves the utilisation of unlabelled data during training. Validated on practical transformer datasets, AG‐SVAF achieves superior performance in terms of diagnostic accuracy and stability, particularly under challenging scenarios involving class imbalance and label scarcity. This approach provides a promising and scalable solution for intelligent condition monitoring in real‐world power system applications.

Source: IET Electric Power Applications

DOI: 10.1049/elp2.70062

Authors: Yanfei Sun, Tao Zhao, Li Gao, Yunpeng Liu

Institution: North China Electric Power University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,880