An online fault diagnosis method for converter transformer based on multi-modal information fusion

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The multimodal information fusion method achieves 96% accuracy in diagnosing transformer faults, significantly improving reliability.
Key metrics show that this method outperforms traditional approaches, demonstrating marked effectiveness in fault detection.
Deep learning techniques, particularly a bidirectional gated neural network, extract essential feature vectors from various data types.
Effective fusion of vibration signals and infrared images suggests a new avenue for enhancing transformer monitoring systems.

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Proposed method improves fault diagnosis accuracy in transformers by 96%, indicating effective use of multimodal data.

Liu et al. (2025) studied this question.

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