This invention discloses a method for building a multimodal fusion-based fault diagnosis model for power transformers, belonging to the field of fault diagnosis model technology. This method deploys multimodal sensors to synchronously acquire
transformer vibration and oscillation wave signals. After preprocessing the signals using Empirical Mode
Decomposition (EMD), it jointly models the time-domain, frequency-domain, and oscillation
wave parameter features. The core of this invention lies in constructing a
graph data model based on physical topology and introducing a cross-
modal Transformer network for deep
feature fusion to capture the complex correlations between different
modes. Furthermore, this method utilizes a variational
autoencoder (VAE) to achieve unsupervised diagnosis and data augmentation for unknown faults; by constructing a power
transformer fault
knowledge graph (KG) and using a TransE model for entity embedding, it achieves accurate
fault severity assessment and lifespan prediction of power transformers.