Affiliations: [a] School of Management, Hefei University of Technology, Hefei, PR. China | [b] Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, PR. China | [c] School of Economics, Hefei University of Technology, Hefei, PR. China | [d]
Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, PR. China
Abstract: Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance.
Keywords: Remaining useful life, support vector regression, phase space reconstruction, variable neighborhood search, aircraft engine
