# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 5 Conclusions  
In this paper, a novel data-knowledge hybrid driven method for gas path fault diagnosis is proposed. It consists of three parts, including a physical model-based GPA method, an ontology based model, and a virtual distance mapping algorithm. Then, a case study of a real GE LM2500 PLUS gas turbine driven natural gas compressor unit is carried out to verify the effectiveness of the proposed diagnosis method. The conclusions are as follows:  
1. Six fault features for three gas path components (including a compressor, two turbines: high pressure turbine and power turbine) are extracted by the physical model-based GPA method. Seven reference fault features for typical gas path fault modes in a compressor and turbine are also summarized. The GPA result of the case study shows that the _DEC_ exception appears (reaching about 5%) at the moment of about 110 s. The GPA method can detect the gas path fault 18 s in advance, compared with the vibration monitoring system. 2. A virtual distance mapping algorithm is proposed to calculate the similar probability between the GPA result and each _FeatureCriteria_ individual. The GPA result is mapped to a specific _FeatureCriteria_ individual in the ontology model, whose similar probability exceeds the threshold of 90%. In the case study, the proposed virtual distance mapping algorithm can effectively identify the most likely compressor _FeatureCriteria_ individual (\(D_{\textit{GPA}}\) = 1% and \(DE_{\textit{ref}}\) = 5%) according to the GPA result. 3. A unified ontology with a hierarchical structure is developed to represent the heterogeneous fault knowledge sources. Three classes (including _FaultCriteria_, _FMECA_, and _MaintenanceAnalysis_) are developed to provide a standard fault diagnosis process. Nine sub-classes are developed to refine the fault classes. Moreover, six SWRL rules are applied for fault reasoning from the mapped _FeatureCriteria_ individual. 4. In the case study, the fault mode of _compressor foreign object damage_ can be effectively identified by the developed ontology model. Moreover, more fault information is reasoned out, including fault effect, fault criticality, fault consequence, and fault detectability. The proper maintenance strategy of \"_condition-based maintenance_\" and maintenance priority of \"_high_\" are also selected. **Author Contributions:** Conceptualization, J.C. and J.L. ; methodology, J.C. and Z.H. ; writing--original draft preparation, J.C. and Z.H. ; software, Z.H. and J.L. ; data curation, J.C. and Z.H. ; formal analysis, J.L. and X.Z. ; writing--review and editing, J.L. and X.Z. ; supervision, H.Z. and D.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Fundamental Research Project (Grant No. JCKY2017208A001, JCKY2019204B009, JCKY2020208B036), and the National Natural Science Foundation of China (Grant No. 51906138, 51876116). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**