Abstract: Abstract: To address the issues of single data sources， low diagnostic accuracy， and slow convergence speeds in existing transformer fault diagnosis methods， this paper proposes a transformer fault diagnosis model based on multi-source data fusion technology and a deep belief network optimized by the black widow algorithm. Firstly， the oil chromatography data are preprocessed； secondly， the deep neural network （DNN） and convolutional neural network （CNN） are used to extract features from the oil chromatography data and the image data， respectively； then the features extracted by DNN and CNN are fused， and the fused features are obtained； next， the fused features are input into the deep belief network （DBN）， and the parameters of the DBN network are optimized using the black widow optimization method， and then the classification results of fault diagnosis are finally output. The final experimental results demonstrate that the proposed method achieves high precision， recall， and accuracy in fault diagnosis， exhibits rapid convergence， and demonstrates strong adaptability to complex diagnostic environments. The average precision， recall， and accuracy rates are 93.67%， 94.32%， and 94.73% respectively， indicating its suitability for transformer fault diagnosis applications.

Key words: Key words: fault diagnosis, deep belief networks, multi-source data fusion, black widow optimization algorithm, transformer