# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
These expert systems make it difficult to select a correct maintenance strategy due to their fragile inference ability. According to the theory of reliability-centered maintenance (RCM), selecting a correct maintenance strategy is a complex task due to the numerous components and their complex interconnections. It requires a number of specialized technologies, i.e., fault criteria analysis, physical structure analysis, failure mode, effects, and criticality analysis (FMECA), and the maintenance logic decision tool. A reliable expert system should enable the involvement of more sufficient fault knowledge from different sources. However, with the increasing heterogeneous knowledge for supporting a correct maintenance decision, it is a challenge to efficiently represent the above fault knowledge based on a unified semantic expression, in support of improving the fault inference ability. In recent years, ontology has been widely applied for knowledge representation and utilization. It enables not only the clear description of knowledge, with explicit specifications, but also the ability to carry out knowledge reasoning using inference engines. [PERSON] et al. [20] developed an ontology model to represent the knowledge from the perspective of FMECA for wind turbines. The ontology model can provide more sufficient information for fault diagnosis and maintenance strategy selection. [PERSON] et al. [21] developed an ontology model to support the fault knowledge representation for a typical pneumatic valve. The ontology model effectively converted the fault propagation mechanism into a shareable and accessible knowledge representation. [PERSON] et al. [22] developed a decision support system to optimize the design parameters for automobile manufacturing. The ontology model was applied to locate the defective components based on the design knowledge from the warranty data. [PERSON] et al. [23] developed an ontology-based reasoning framework to enhance the maintenance decisions for a petroleum plant. [PERSON] et al. [24] built an ontology model to identify the optimal maintenance activitiesfor an air compressor. The concepts of current trends were also considered in the ontology model to trigger the predictive maintenance strategy. [PERSON] et al. (2015) proposed a maintenance-related ontology model that includes the concepts of maintenance and production activities. The joint decisions of maintenance and production could be reasoned out by the ontology model. The above studies have proven that the ontology technology is efficient in knowledge representation, sharing, and reasoning for an intelligent expert system. Several investigations have been carried out for implementing task 1 or task 2 of the gas path fault diagnosis process. In task 1, the GPA method has proved its effectiveness in field data analysis. In task 2, the ontology model is a powerful alternative to developing an intelligent expert system for knowledge-based reasoning. However, most of the previous studies assumed that task 1 and task 2 are isolated and are not integrated to cover the whole gas path diagnosis process. In practice, the gas path diagnosis is difficult to implement if the expert system cannot work with the domain-specific data analysis. The purpose of this work is to propose a data-knowledge hybrid driven method for an intelligent gas path diagnosis system. The contribution is that the hybrid driven method supports the whole gas path diagnosis process, which includes field data analysis (task 1) and knowledge-based reasoning (task 2). Firstly, a physical model-based GPA method is used to extract the fault feature from the field data in task 1. Moreover, an ontology-based model is applied to represent the massive fault knowledge from different sources. With the defined reasoning rules, the fault information and correct maintenance strategy can be obtained with the input of the identified fault feature criteria individual in task 2.