- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Clean energy is an increasing concern as more and more countries pay attention to environmental protection, which brings the rapid development of wind power. More new wind farms and new wind turbines have been put into operation, this caused the problem that the diagnostic knowledge is lacking and diagnostic efficiency is low for new employed maintenance personnel. In order to meet the demands of fault diagnosis of wind turbines, a method of intelligent fault diagnosis based on ontology and FMECA (Failure Mode, Effects and Criticality Analysis) is proposed in this paper. In the proposed method, the FMECA of wind turbines is selected as the knowledge source, and deep knowledge and shallow knowledge extracted from this source are represented by ontology modeling. And then, the diagnosis knowledge base of wind turbines can be established. Reasoning on this knowledge base by virtue of JESS (Java Expert Shell System) rule engine, maintenance personnel can find the causes of faults of a wind turbine quickly, and choose the proper solutions. This method realizes the knowledge sharing between product design enterprises and wind farms. The knowledge base which combines the deep knowledge and the shallow knowledge can improve the capability of fault diagnosis and provide better supports for diagnostic decision making.
7. Conclusions and future enhancements
The reasoning framework for intelligent fault diagnosis of wind turbines based on ontology and FMECA is proposed in this paper. By virtue of OWL and SWRL, the deep knowledge and the shallow knowledge, which are extracted from FMECA, are modeled in the form of ontology, and then the knowledge is translated into the facts and rules that are available to a reasoner. With the JESS rule engine, maintenance personnel can be supplied with the information of the failure causes, the locations and the diagnosis methods during fault diagnosis process. This method achieves knowledge sharing and reuse between product design enterprises and wind farms, it is suitable to provide supports to fault diagnosis, especially for new wind farms and new wind turbines.