Intelligent Fault Diagnostic Model for Industrial Equipment Based on Multimodal Knowledge Graph

Industrial equipment failure diagnosis is a crucial issue that impacts the national industrial manufacturing level, economic cycle development, and sustainable technological advancement. A multimodal knowledge graph (MMKG)-based intelligent diagnostic model for industrial equipment fault is proposed to address the issues of insufficient and inadequate fault data samples encountered when using a single-mode model for fault diagnosis in existing industrial equipment. This model does not require extensive data learning for equipment fault diagnosis in complex industrial scenarios. The model utilizes an improved faster region with CNN (Faster RCNN) features the object detection module to extract visual information feature vectors of semiordered main and nonmain objects. These feature vectors are then mapped to entity, attribute, and relationship vectors in a knowledge graph using cosine similarity for feature correspondence mapping. The semantic matching inference is performed based on this mapping, resulting in a set of fault triplets. Finally, the bidirectional and autoregressive transformers (BARTs) text generation model processes this triplet set to generate fault diagnosis texts. Experimental results demonstrate that the improved Faster RCNN object detection model achieves a 1.2% increase in confidence when trained with small training datasets. The accuracy of generated fault description texts reaches approximately 98% compared to standard texts. The model presented in this article addresses the challenge of diagnosing faults in industrial equipment, particularly in complex scenarios with limited data, such as substations. It enhances the target detection model to effectively extract visual features even when data is scarce. Additionally, it utilizes an MMKG to enable interpretable intelligent decision-making.