A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

Wu, Yuejia; Zhou, Jian-Tao

doi:10.1109/cscwd57460.2023.10152706

Cited by 1 publication

(1 citation statement)

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“…Traditional methods have encountered limitations, such as an inadequate grasp of graph structures and insufficient learning of data features, leading to compromised reasoning precision 14 . To overcome these barriers, novel strategies involving multi‐head self‐attention mechanisms have been introduced, bolstering reasoning accuracy by honing feature learning through multiple attentional standpoints 15 …”

Section: Related Workmentioning

confidence: 99%

MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis

Chen,

Wang

2024

Computational Intelligence

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As industrial production escalates in scale and complexity, the rapid localization and diagnosis of equipment failures have become a core technical challenge. In response to the demand for intelligent fault diagnosis in large‐scale industrial equipment, this study presents “MultiCogniGraph”—a multi‐hop reasoning diagnostic method that integrates multimodal data fusion, knowledge graphs, and graph convolutional networks (GCN). This method leverages internet of things (IoT) sensor data, small‐sample imagery, and expert knowledge to comprehensively characterize the equipment state and accurately detect subtle distinctions in fault patterns. Utilizing a knowledge graph to synthesize data from multiple sources and deep reasoning with GCN, “MultiCogniGraph” achieves swift and effective fault localization and diagnosis. The integration of these techniques not only enhances the efficiency and accuracy of fault diagnosis but also its interpretability, marking a new direction in the field of intelligent fault diagnostics.

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Section: Related Workmentioning

confidence: 99%