@article{scholars13835,
            note = {cited By 0; Conference of 2nd International Conference of Aerospace and Mechanical Engineering, AeroMech 2019 ; Conference Date: 20 November 2019 Through 21 November 2019; Conference Code:241099},
             doi = {10.1007/978-981-15-4756-0{$_3$}{$_8$}},
            year = {2020},
           title = {Analysis of the impact of degradation on gas turbine performance using the support vector machine (svm) method},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Lecture Notes in Mechanical Engineering},
           pages = {441--451},
        abstract = {Degradation is an important aspect in the operation and maintenance of gas turbines since it affects maintenance costs substantially. Hence, the study of degradation in terms of recoverable and non-recoverable degradation is crucial to formulate a correct maintenance strategy and, as a result, achieve optimum maintenance cost. In this paper, the impact of recoverable and non-recoverable degradation towards compressor discharge pressure, fuel gas flow, and exhaust gas temperature are measured during the start of run period that reflects the time period from the new gas turbine condition to the first scheduled offline crank wash, which normally approximates to 8000 running hours. For the study, a three-unit single speed light industrial gas turbine that drives an electrical generator to power up an offshore platform located in a tropical climate is considered. The measurement of the parameters has been conducted using the support vector machine (SVM) method. {\^A}{\copyright} Springer Nature Singapore Pte Ltd 2020.},
        keywords = {Costs; Flow of gases; Gas compressors; Gas turbines; Gases; Maintenance; Offshore oil well production, Compressor discharge pressures; Fuels gas; Gas turbine performance; Maintenance cost; Maintenance strategies; Non-recoverable degradation; Operations and maintenance; Recoverable degradation; Support vector machine method; Support vectors machine, Support vector machines},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087023146&doi=10.1007\%2f978-981-15-4756-0\%5f38&partnerID=40&md5=e6739eaf8ade742d4078799f15cc8628},
            isbn = {9789811547553},
            issn = {21954356},
          author = {Asnawi, K. F. B. A. and Lemma, T. A.}
}