What question did this study set out to answer?

To develop a robust deep learning framework for accurate fault diagnosis in power transformers by integrating gas analysis and vibration data.

March 14, 2026International Journal of Information and Communication Technology0 citationsOpen Access

Deep learning-based power transformer condition monitoring and fault diagnosis algorithm

WCWei Chen

Key Points

To develop a robust deep learning framework for accurate fault diagnosis in power transformers by integrating gas analysis and vibration data.
Created an adaptive deep learning model using convolutional neural networks and bidirectional long short-term memory
Processed dissolved gas analysis profiles and vibration signatures for fault diagnosis
Implemented an attention mechanism to enhance feature interpretability and accuracy
Evaluated the model on IEC dissolved gas analysis dataset and real vibration records
Achieved 98.2% diagnostic accuracy, outperforming support vector machines (85.1%) and deep belief networks (90.8%)
Demonstrated robustness in detecting incipient faults under various conditions
Attention weights aligned with known failure mechanisms, enhancing reliability and interpretability

Abstract

Power transformers are critical in smart grids, but accurate fault diagnosis remains challenging due to limitations in conventional dissolved gas analysis and data-intensive deep learning methods.This paper introduces an integrated deep learning framework combining convolutional neural networks with bidirectional long short-term memory via an adaptive multi-head attention mechanism.The model jointly processes dissolved gas analysis profiles and vibration signatures: convolutional layers extract spatial features from gas data, while bidirectional units capture temporal vibration dynamics.The attention mechanism dynamically highlights discriminative features, improving both interpretability and accuracy.Evaluated on the IEC dissolved gas analysis dataset and real vibration records, our method achieves 98.2% diagnostic accuracy, surpassing support vector machines (85.1%) and deep belief networks (90.8%).Additional tests confirm robustness in detecting incipient faults under varied conditions.Attention weights align with known failure mechanisms, offering a reliable, interpretable tool for predictive maintenance.

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