  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 using two
industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow
Facility (MFF). Compared to existing methods, Root-KGD not only gives more
accurate root cause variable diagnosis results but also provides interpretable
fault-related information by locating faults to corresponding physical entities
in knowledge graph (such as devices and streams). In addition, combined with
its lightweight nature, Root-KGD is more effective in online industrial
applications.
