# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 3 Methodology
### Ontology Based Model for Gas Path Fault Reasoning  
#### 3.2.1 Development of the Fault Diagnosis Ontology Model  
Ontology is a powerful technology for organizing massive heterogeneous knowledge and logical relations [28]. On the one hand, ontology provides the unified formalizing vocabularies to explicitly represent the heterogeneous fault knowledge, avoiding the divergence and misunderstanding between different domain engineers. On the other hand, ontology guarantees the fault reasoning ability by comprehensively analyzing the knowledge relations [29]. There are several domains of technologies to provide the knowledge for fault reasoning, such as fault criteria analysis, physical structure analysis, FMECA, and the maintenance logic decision tool. The heterogeneous fault knowledge involves massive numbers of terminologies. Consequently, a unified ontology with a hierarchical structure for gas path fault diagnosis is constructed to address the problem of heterogeneous terminologies. Moreover, the ontology can be updated and extended flexibly with the hierarchical architecture, as shown in Figure 3. In the unified ontology, three classes (including _FaultCriteria_, _FMECA_, and _MaintenanceAnalysis_) are applied to provide a standard fault diagnosis process. The details of the classes are described as follows. 1. _FaultCriteria_ represents the knowledge from the fault criteria analysis of each gas path fault mode. It has the sub-classes of _FeatureCriteria_ and _FaultMode_. The _FeatureCriteria_ sub-class represents the reference fault feature of each gas path fault mode. The _FaultMode_ sub-class represents the typical gas path fault modes of a gas turbine. 2. _FMECA_ represents the knowledge from physical structure analysis and FMECA. It has the sub-classes of _FaultLocation_, _FaultEffect_, _FaultCrriticality_, _FaultConsequence_, _FaultDetectablity_. The fault information is stored and reasoned in the above sub-classes. 3. _MaintenanceAnalysis_ represents the knowledge from the maintenance logic decision tool. It has a sub-class of _MaintenanceStrategy_ and _MaintenancePriority_. The correct maintenance strategy of a fault mode is selected and stored in the _MaintenanceStrategy_ sub-class. The implementation priority of the selected maintenance strategy is stored in the _MaintenancePriority_ sub-class. \begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Feature** & **Description** \\ \hline _DGC_ & Flow rate degradation of compressor \\ _DEC_ & Efficiency degradation of compressor \\ _DGT_ & Flow rate degradation of high pressure turbine \\ _DET_ & Efficiency degradation of high pressure turbine \\ _DGP_ & Flow rate degradation of power turbine \\ _DEP_ & Efficiency degradation of power turbine \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of six gas path fault features. In this work, the fault diagnosis ontology model for task 2 is established according to the unified ontology with hierarchical structure (shown in Figure 3). The OWL is applied to encode the fault diagnosis ontology model. Four components are selected based on the constitution of the OWL ontology [30], which can be defined as follows. \[Ontology=\sum\{\textit{Classes},\textit{Individuals},\textit{Attributes},\textit{ Relations}\} \tag{3}\]  
where \(\sum\{\}\) denotes the collection of entities in the ontology. The detailed descriptions of the four components are listed as follows. * _Classes_: types of objects, or kinds of things. * _Individuals_: instances of different _Classes_. * _Attributes_: aspects, properties, features, and characteristics of _Classes_. * _Relations_: relations between different _Classes_ or individuals. The ontology model can be expressed as follows. \[OWL:\textit{Thing}=\textit{Classes}\sum\left(FaultCriteria,\textit{FMECA}, \textit{MaintenanceAnalysis}\right) \tag{4}\]  
The classes of _FaultCriteria_, _FMECA_, and _MaintenanceAnalysis_ can be described as follows.