A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
Source abstract
Abstract
Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Engineering Diagnostics and Reliability · Risk and Safety Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiaotong University, Shanghai 200240, China
School of Engineering, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
Funding
the National Fundamental Research Project · the National Natural Science Foundation of China
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