title: [Research on engine remaining useful life prediction based on oil spectrum analysis and particle filtering].
abstract: The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the
authors.affiliation: Equipment Command and Management Department, Ordnance Engineering College, Shijiazhuang 050003, China. waterprison@163.com
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.affiliation: Equipment Command and Management Department, Ordnance Engineering College, Shijiazhuang 050003, China.
authors.forename: Yun-xian
authors.initials: YX
authors.lastname: Jia
authors.affiliation: Equipment Command and Management Department, Ordnance Engineering College, Shijiazhuang 050003, China.
authors.forename: Li-ying
authors.initials: LY
authors.lastname: Cai
authors.affiliation: Equipment Command and Management Department, Ordnance Engineering College, Shijiazhuang 050003, China.
authors.forename: Guo-yu
authors.initials: GY
authors.lastname: Lin
authors.affiliation: Equipment Command and Management Department, Ordnance Engineering College, Shijiazhuang 050003, China.
authors.forename: Jin-song
authors.initials: JS
authors.lastname: Zhao