TY - JOUR KW - Corrosion; Forecasting; Gases; Maintenance; Monte Carlo methods; NASA KW - CMAPSS; Condition based maintenance; Particle filter; Prognostics; Reduced maintenance costs; Reliability and availability; Remaining useful life predictions; Remaining useful lives KW - Gas turbines ID - scholars11731 N2 - Gas turbines are commonly used in distributed power generation. Because of high speed nature, they require good maintenance for increased reliability and availability. Remaining useful life prediction is therefore an essential part of condition-based maintenance to better foresee future state hence guaranteeing design efficiency, reduced maintenance cost, and improved safety. Gas turbines also contain a lot of sensors data that need to be processed for better prediction. In this paper, a probabilistic approach called particle filter is used for prediction. The proposed approach is tested using Turbofan degradation data provided by NASA as a benchmark problem. Meanwhile, through time the gas turbines experiences a change from normal state to degraded state attributed to aging, corrosion and erosion etc. Hence, in the context of abundant data, it is helpful to know the transition between states. For the same reason, the present paper suggests a statistical approach called Z-test. The test results show that the proposed technique provides score and MAPE values of 559.9 and 21.6 respectively, comparable to past reported performance. © 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim IS - 3 VL - 50 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062657843&doi=10.1002%2fmawe.201800219&partnerID=40&md5=811cd85dcd56216c2af53f811d74a5e0 A1 - Ahsan, S. A1 - Lemma, T.A. A1 - Muhammad, M. JF - Materialwissenschaft und Werkstofftechnik Y1 - 2019/// SP - 336 TI - Prognosis of gas turbine remaining useful life using particle filter approach Prognose der verbleibenden Nutzungsdauer einer Gasturbine mittels Partikelfilter-Verfahren N1 - cited By 10 AV - none EP - 345 SN - 09335137 PB - Wiley-VCH Verlag ER -