# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 3 Methodology
### Ontology Based Model for Gas Path Fault Reasoning
#### 3.2.2 Development of Reasoning Rules in the Ontology Model  
Task 2 of the diagnosis process is to provide diversified fault information and to select a correct maintenance strategy from the mapped fault criteria individual. Therefore, the querying and reasoning functions should be supported to enable in-depth knowledge  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Object Property** & **Description** \\ \hline _FC_has_FMd_ & Relation between feature criteria and its fault mode according to the fault criteria analysis \\ _FMd_has_FMs_ & Relations between fault mode and fault maintenance strategy according to the logical decision-making \\ _FMd_has_FDet_ & Relations between fault mode and fault detectability according to the FMECA \\ _FMd_has_FCri_ & Relations between fault mode and fault criticality according to the FMECA \\ _FMd_has_FE_ & Relations between fault mode and fault effect according to the FMECA \\ _FMd_has_FCon_ & Relations between fault mode and fault consequence according to the FMECA \\ _FE_has_FL_ & Relations between fault effect and fault location according to the physical structure analysis \\ _FMs_has_FMp_ & Relations between maintenance strategy and maintenance priority \\ _FCri_has_FMP_ & Relations between fault criticality and maintenance priority (They have the same level value.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main object properties in the developed fault diagnosis ontology model.  
Figure 4: Relations between the sub-classes in the developed fault diagnosis ontology model.  
reasoning for gas path fault diagnosis. Here, SWRL is used to establish an enhanced reasoning engine for the gas path diagnosis.  
According to the predicate logic, the SWRL rules are written to assign the subject, predicate, and object [31]. The implicit fault knowledge can be discovered with the SWRL rules, realizing in-depth fault knowledge reasoning. The designed gas path fault reasoning engine based on the SWRL rules is shown in Figure 5.  
As shown in Figure 5, the mapped specific _FeatureCriteria_ individual is considered as the input of the fault diagnosis reasoning process in task 2. There are five reasoning scenarios to determine the fault information and the correct maintenance strategy.  
**Rule 1** is used to identify the corresponding fault mode, with the input of the mapped _FeatureCriteria_ individual reasoned out by the **mapping rule**, which can be express as follows.  
_hasHappened(?FCta,true)'FeatureCriteria(?FCta)'FaultMode(?FMd)'FCta_has_FMd (?FCta,?FMd)->hasHappened(?FMd,true)_  
**Rule 2** reasons out the fault information (such as fault criticality, fault consequence, fault detectability, fault effect, etc.) with the input of the identified _FaultMode_ individual from **Rule 1**, which can be express as follows.  
_hasHappened(?FMd,true)'FMd_has_FCri(?FMd,?FCri)'FMd_has_FCon(?FMd,?FCon)_  
_'FMd_has_FCri(?FMd,?FCri)'FMd_has_FE(?FMd,?FE)->hasHappened(?FCri,true)'hasHappened(?FCri,true)'hasHappened(?FCri,true)'hasHappened(?FE,true)_  
**Rule 3** captures the fault location information, with the input of identified _FaultEffect_ individual from **Rule 2**, which can be express as follows.  
_hasHappened(?FE,true)'FaultLocation(?FL)'FaultEffect(?FE_has_FL(?FE,?FL)_  
_->hasHappened(?FL,true)_  
**Rule 4** selects a proper maintenance strategy with the inputs of _FaultDetectability_, _FaultCriterality_, and _FaultConsequence_ individuals from **Rule 2**, which can be express as follows.  
_hasHappened(?FCri,true)'hasHappened(?FCon,true)'hasHappened(?FCt,true)'MaintenanceStrategy(?FMd)'MaintenanceStrategy(?FMd)'FCri_has_FMs(?FCri,?FMd)'FCon_has_FMs(?FCri,?FMd)'FCot_has_FMs(?FCri,?FMd)'FCot_has_FMs(?FCot,?