Multimodal Defect Recognition for Electrical Transformers Based on Improved Dempster–Shafer Evidence Fusion

Abstract

Electrical transformer diagnosis combines evidence with different observation capabilities. We present a decision-level fusion implementation using Chinese BERT inspection-text classification, a multi-scale one-dimensional CNN for dissolved gas analysis, and YOLOv11n image detection. Capability masks, reliability weighting, fuzzy evidence attenuation and Murphy fusion produce structured diagnostic records. On separated retrospective tests, the CNN achieved 97.14% accuracy (macro-F1 0.9700) on 629 DGA records and BERT achieved 72.45% accuracy (macro-F1 0.7466) on 715 reserved log lines. On a five-seed visual stress test, BERT+YOLO achieved 96.84% mean accuracy, while the complete fusion achieved 71.69% with 74.75% coverage under the fixed rejection policy. The method’s principal strengths are explicit modality capability modeling, traceable evidence exchange and independently replaceable lightweight classifiers. Results are interpreted within the documented corpus and pairing protocol; event-level field validation and embedded-device benchmarking are identified as the next deployment studies.