Root-KGD: A Novel Framework for Root Cause Diagnosis Based on Knowledge Graph and Industrial Data
Jiyu Chen, Jinchuan Qian, Xinmin Zhang, Zhihuan Song

TL;DR
Root-KGD is a new framework that combines knowledge graphs and industrial data to improve the accuracy, interpretability, and online applicability of root cause diagnosis in complex industrial processes.
Contribution
It introduces a novel approach that integrates domain knowledge and data-driven modeling using knowledge graph reasoning for fault diagnosis.
Findings
More accurate root cause variable diagnosis compared to existing methods.
Provides interpretable fault-related information by locating faults in the knowledge graph.
Effective for online industrial fault diagnosis due to its lightweight design.
Abstract
With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated…
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Taxonomy
TopicsMineral Processing and Grinding · Fault Detection and Control Systems · Industrial Vision Systems and Defect Detection
