Multi-stage degradation modeling for airborne fuel pump based on LSTAR
LI Juan1,2, JING Bo1, JIAO Xiaoxuan1, LIU Xiaodong3,4, DAI Hongde5
1. College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038, China;
2. College of Mathematics and Statistics, Ludong University, Yantai 264025, China;
3. China Aviation Industry Jincheng Nanjing Electrical and Hydraulic Engineering Research Center, Nanjing 210000, China;
4. Aviation Science and Technology Key Laboratory of Aviation Mechanical and Electrical System, Nanjing 210000, China;
5. Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China
Abstract：The performance degradation of airborne fuel pump is nonlinear and multi-stage with stationary-accelerated-stationary degradation pattern. The existing degradation models are unsuitable for the modeling of this degradation problem in life cycle, so the signal output from the pressure sensor attached to the fuel pump is modeled based on the logistic smooth transition auto-regression (LSTAR) model. First, auto-regressive (AR) model was established for the converted pressure signal, the necessity of the LSTAR model was examined by nonlinear test, and parameters of the model was estimated by nonlinear least square method. The transfer variable was chosen by minimizing the AIC value and maximizing the goodness of fit. Adaptive test and normality test of the model have been done based on residual analysis. The results show that the LSTAR based method is superior to the AR model. The dividing of the degradation stage and the modeling problem are solved by the presented method, which lays better foundation for the prognostics and health management (PHM) of airborne fuel pump.
李娟, 景博, 焦晓璇, 刘晓东, 戴洪德. 基于LSTAR的机载燃油泵多阶段退化建模[J]. 北京航空航天大学学报, 2017, 43(5): 880-886.
LI Juan, JING Bo, JIAO Xiaoxuan, LIU Xiaodong, DAI Hongde. Multi-stage degradation modeling for airborne fuel pump based on LSTAR. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2017, 43(5): 880-886.
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2017, Vol. 43 
