TY  - CONF
VL  - 131
PB  - EDP Sciences
AV  - none
ID  - scholars8258
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033223217&doi=10.1051%2fmatecconf%2f201713104014&partnerID=40&md5=c32b340a4dec5b30e0823038ee6687d7
A1  - Ahsan, S.
A1  - Alemu Lemma, T.
N1  - cited By 11; Conference of 2017 UTP-UMP Symposium on Energy Systems, SES 2017 ; Conference Date: 26 September 2017 Through 27 September 2017; Conference Code:131396
N2  - Gas turbine (GT) engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR) models to predict remaining useful life (RUL) of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT. © The authors, published by EDP Sciences, 2017.
SN  - 2261236X
KW  - Engines; Forecasting; Marine applications; Marine engineering; Marine engines; NASA; Turbofan engines
KW  -  Auto regressive models; Complex machines; High availability; Model parameters; Parametric investigations; Pessimistic value; Remaining useful life predictions; Remaining useful lives
KW  -  Gas turbines
TI  - Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model
Y1  - 2017///
ER  -