Computer Science ›› 2015, Vol. 42 ›› Issue (6): 23-27.doi: 10.11896/j.issn.1002-137X.2015.06.005

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Research of Data Driven Method for Gas Turbine Trip Prediction

XIE Chen, WANG Rui-zhi, LI Yang, MIAO Duo-qian and JIAO Na   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Gas turbine is the most widely used device for modern industry.Once trips happened,gas turbine engines could cost customers millions dollars.Research on diagnosis and prediction of trips has significant impact.However,prediction of gas turbine trips is a relatively new subject and research finding is limited.So far no data driven solution for prediction of gas turbine trips is literately reported.The research work begines from preprocessing the data:normalization,dimensionality reduction,attribute value resampling and granulating.Experiments were conducted intensively on real datasets by using data-driven prediction methods Elman.The results of experiments on how to set up a better Elman network are valuable to other relative research.

Key words: Data driven,Elman,Fault prediction,Gas turbine,Trip

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