汽车工程 ›› 2020, Vol. 42 ›› Issue (1): 108-113.doi: 10.19562/j.chinasae.qcgc.2020.01.016

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基于特征评价的发动机寿命预测方法研究

谷广宇1, 刘建敏1, 乔新勇1, 姜红元2, 杨浩2   

  1. 1.陆军装甲兵学院车辆工程系,北京 100072;
    2.73089部队保障部,徐州 221004
  • 收稿日期:2018-12-27 发布日期:2020-01-21
  • 通讯作者: 刘建敏,教授,博士,E-mail:119792651@qq.com

Research on Engine Life Prediction Method Based on Characteristics Evaluation

Gu Guangyu1, Liu Jianmin1, Qiao Xinyong1, Jiang Hongyuan2, Yang Hao2   

  1. 1.Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072;
    2.Security Department of Unit 73089, Xuzhou 221004
  • Received:2018-12-27 Published:2020-01-21

摘要: 针对发动机性能评估及预测过程中需要的特征参数多,且对特征参数的评价及优选标准不统一的问题,提出了基于多指标综合评价的发动机状态特征参数优选方法。以某型装备发动机为研究对象,提取了原始的12个状态特征参数,建立了相关性、单调性、离散性等一系列评价指标,实现对状态特征的客观评价,并提出利用熵权法,从信息量角度对多指标客观定权,通过理想点及相似度,实现对状态特征参数的优选排序。最后通过相似度指标对优选后的特征参数进行了加权融合,建立了过程模糊规则(PFR)模型,实现了该型发动机使用寿命的预测。

关键词: 发动机, 寿命预测, 熵权, 理想点, 特征评价, 过程模糊规则(PFR)

Abstract: For the problem of the large quantity of characteristics needed in the process of engine performance evaluation and prediction, and the non-uniform standard of optimal selection and evaluation of characteristic parameters, this paper presents an optimal selection method of engine state characteristic parameters based on multi-index comprehensive evaluation. A certain engine is taken as the research object, and 12 original characteristics parameters of this engine are analyzed to establish a series of evaluation indicators from the aspects of correlation, monotonicity, discreteness, etc. to realize object evaluation of the state characteristics. The entropy weight is proposed to determine the weight of multiple indexes objectively in the perspective of information content. And the optimal ranking of the state characteristic parameters is realized through ideal points and similarity. By weighted fusion of the selected feature parameters by similarity index, the progress fuzzy rule model is established and the engine life prediction is realized

Key words: engine, life prediction, entropy, ideal point, feature evaluation, PFR