Knowledge modeling of fault diagnosis for turbine generator sets based on ontology

Abstract

Abstract: Since the knowledge representation method of fault diagnosis in turbine generator sets is lack of complete in the field of terms, complex terminology, system heterogeneity and difficulty in sharing and reuse etc, a new method for ontology-based knowledge representation is well adopted in terms of fault diagnosis requirements. Methods for ontology construction and a knowledge representation model for the fault diagnosis of turbine generator sets are proposed respectively. The model defines its ontology concepts, attributes, relationships, examples and axioms, and provides a clear formal specification for knowledge representation. Furthermore, Protégé_4.3 is used to construct the ontology of turbine generator sets fault diagnosis domain with fault type, fault characteristic, fault reason and maintenance strategy, and the knowledge is proved to be consistent in the ontology by designing algorithms. Turbine generator sets fault is simulated by SQI mechanical fault comprehensive simulation test bench, and the reasoning test of ontology knowledge is verified by the inference engine named FaCT++. The result indicates that the knowledge model of ontology-based fault diagnosis is feasible.

Key words: turbine generator sets, fault diagnosis, knowledge modeling, consistency test, ontology reasoning

CLC Number:

TH17
TP182

YAN Chang-feng, LI Yu, WANG Hui-bin, ZHANG Qiang AI Ke-yong, WU Li-xiao. Knowledge modeling of fault diagnosis for turbine generator sets based on ontology[J]. Journal of Lanzhou University of Technology, 2020, 46(5): 41-48.

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