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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.

I-PixelHop is a lightweight learning framework based on successive subspace learning. Compared with the deep learning methods, the rolling bearing fault diagnosis (RBFD) model based on I-PixelHop has lower computational complexity and smaller model size, and it can obtain a good diagnosis accuracy in the complex working conditions. However, the training and diagnosis time of the RBFD model based on I-PixelHop are still long in the face of large-scale RB fault datasets. Therefore, an efficient I-PixelHop framework based on Spark-GPU for intelligent fault diagnosis is proposed. First, a Spark-GPU-based distributed parallel I-PixelHop framework is developed, which can efficiently perform distributed parallel training and diagnosis. Second, an asynchronous parallel execution strategy based on superscalar pipeline (APES-SP) is proposed, which can reduce the waiting time of each functional unit of the distributed parallel I-PixelHop framework. Finally, an ensemble classifier based on Bagging is designed and parallelized, which can improve the diagnosis accuracy of the RBFD model based on distributed parallel I-PixelHop framework. Extensive experiments demonstrate that the proposed framework can not only maintain high diagnosis accuracy but also significantly improve the training performance and diagnosis performance of the RBFD model based on I-PixelHop under industrial big data.