@inproceedings{scholars7242,
           title = {Methodology for short-term performance prognostic of gas turbine using recurrent neural network},
            note = {cited By 12; Conference of IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015 ; Conference Date: 6 December 2015 Through 9 December 2015; Conference Code:119262},
          volume = {2016-J},
             doi = {10.1109/IEEM.2015.7385755},
       publisher = {IEEE Computer Society},
         journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
           pages = {787--791},
            year = {2016},
            issn = {21573611},
          author = {Muhammad, M. and Mohammadreza, T. B. and Karim, Z. A. A.},
            isbn = {9781467380669},
        keywords = {Condition monitoring; Gas turbines, Condition based maintenance; Data-driven prognostics; Equipment performance; Industrial gas turbines; Maintenance strategies; Monitoring applications; performance prognostic; Recurrent neural network (RNN), Recurrent neural networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990540&doi=10.1109\%2fIEEM.2015.7385755&partnerID=40&md5=9133895e419a5db8ad4ab7fc810ffef5},
        abstract = {The issue of performance prognosis has been a topic of considerable interest in industrial condition monitoring applications. An innovative data driven prognostic methodology has been introduced in the current study by utilizing artificial recurrent neural network (RNN) approach which intends to improve the capability of equipment performance prediction within a specified short time bound even with limited available data. The ability of the approach is demonstrated using condition monitoring parameters collected from a 20 MW industrial gas turbine. An appropriate selection and fusion of measured variables has been employed to feed RNN with the most influential performance information. The analysis demonstrated that the developed prognostic approach has a great potential to provide an accurate short term forecast of equipment performance which can be invaluable for maintenance strategy and planning. {\^A}{\copyright} 2015 IEEE.}
}