北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (5): 880-886.doi: 10.13700/j.bh.1001-5965.2016.0347

• 论文 • 上一篇    下一篇

基于LSTAR的机载燃油泵多阶段退化建模

李娟1,2, 景博1, 焦晓璇1, 刘晓东3,4, 戴洪德5   

  1. 1. 空军工程大学 航空航天工程学院, 西安 710038;
    2. 鲁东大学 数学与统计科学学院, 烟台 264025;
    3. 中航工业金城南京机电液压工程研究中心, 南京 210000;
    4. 航空机电系统综合航空科技重点实验室, 南京 210000;
    5. 海军航空工程学院 控制工程系, 烟台 264001
  • 收稿日期:2016-04-28 出版日期:2017-05-20 发布日期:2017-05-27
  • 通讯作者: 李娟,E-mail:daidaiquanquan123@126.com E-mail:daidaiquanquan123@126.com
  • 作者简介:李娟,女,博士研究生,讲师。主要研究方向:故障预测与健康管理;景博,女,博士,教授,博士生导师。主要研究方向:故障预测与健康管理、可测试性设计、传感器网络、数据融合。
  • 基金资助:
    航空科学基金(201428960221)

Multi-stage degradation modeling for airborne fuel pump based on LSTAR

LI Juan1,2, JING Bo1, JIAO Xiaoxuan1, LIU Xiaodong3,4, DAI Hongde5   

  1. 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
  • Received:2016-04-28 Online:2017-05-20 Published:2017-05-27
  • Supported by:
    Aeronautical Science Foundation of China (201428960221)

摘要: 机载燃油泵的性能退化呈现出平稳—加速—平稳的非线性、多阶段模式,针对现有退化模型难以准确描述其全寿命周期性能退化的问题,以逻辑平滑转换自回归(LSTAR)模型为工具,对机载燃油泵出口压力传感器信号进行建模。首先,对转换后的压力传感器信号建立自回归 (AR)模型,通过非线性检验说明建立LSTAR模型的必要性;然后,应用非线性最小二乘法完成参数估计;最后,在AIC准则最小及拟合优度最大的原则下,选择转换变量,通过残差进行模型的适应性检验与正态性检验。结果表明:基于LSTAR模型的拟合精度明显优于线性自回归模型。本文提出的方法成功解决了机载燃油泵性能退化的多阶段准确建模问题,为机载燃油泵的预测与健康管理 (PHM)奠定了坚实的基础。

关键词: 燃油泵, 传感器, 预测与健康管理(PHM), 逻辑平滑转换自回归(LSTAR)模型, 退化建模

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.

Key words: fuel pump, sensor, prognostics and health management (PHM), logistic smooth transition auto-regression (LSTAR) model, degradation modeling

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