CDFMD: Causal Dynamic Fusion Reasoning-Based Multimodal Intelligent Fault Diagnosis Model for Power Transformers

With the continuous advancement of intelligence in power systems, traditional unimodal fault diagnosis methods can no longer satisfy the demand for precise monitoring of complex power equipment. To address the challenges of multimodal data fusion and fault diagnosis in intelligent sensing scenarios, this paper proposes a multimodal intelligent diagnosis model for power transformers based on causal dynamic fusion reasoning. By introducing a causal reasoning mechanism, the proposed model overcomes the limitations of conventional multimodal fusion approaches that rely solely on statistical correlations. A four-layer architecture is constructed, consisting of a Causal Dynamic Fusion layer, a Graph Reasoning layer, a State Prediction layer, and a Meta-Reinforcement Learning Optimizer, thereby forming a complete closed-loop framework from multimodal feature extraction to intelligent diagnostic decision-making. This study focuses on key issues including causal discovery and dynamic fusion in multimodal data, cross-sample contextual enhancement, equipment state prediction, and early warning. Performance evaluation experiments are conducted on a large-scale synchronized dataset containing image, audio, and time-series modalities. Experimental results demonstrate that the proposed CDFMD model outperforms conventional methods in diagnostic accuracy and real-time performance, providing a novel technical pathway for intelligent operation and maintenance of power equipment.