A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

• Main Authors: Delong Feng, Mingqing Xiao, Yingxi Liu, Haifang Song, Zhao Yang, Lei Zhang
• Format: Article
• Language: English
• Published: SAGE Publishing 2016-05-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814016650169

Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis–based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included), and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.