航空学报 > 2022, Vol. 43 Issue (10): 527347-527347   doi: 10.7527/S1000-6893.2022.27347

失效物理与数据调制融合的航空液压泵寿命估计

王少萍1,2, 耿艺璇1, 石存1   

  1. 1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191;
    2. 北京航空航天大学 宁波创新研究院, 宁波 315800
  • 收稿日期:2022-04-29 修回日期:2022-05-15 发布日期:2022-06-08
  • 通讯作者: 王少萍,E-mail:shaopingwang@vip.sina.com E-mail:shaopingwang@vip.sina.com
  • 基金资助:
    国家自然科学基金国际重点合作项目(51620105010);国家科技重大专项经费资助(J2019-V-0016-0111)

Life estimation of aircraft hydraulic pump based on failure physics and data driven

WANG Shaoping1,2, GENG Yixuan1, SHI Cun1   

  1. 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;
    2. Ningbo Institute of Technology, Beihang University, Ningbo 315800, China
  • Received:2022-04-29 Revised:2022-05-15 Published:2022-06-08
  • Supported by:
    NSFC Projects of International Cooperation and Exchanges (51620105010); National Science and Technology Major Project (J2019-V-0016-0111)

摘要: 航空液压泵寿命长且寿命期大多在外场使用阶段,仅使用内场试验数据无法得到准确的寿命估计指标。而液压泵外场使用观测数据具有多种不确定性,且与内场施加的载荷谱不一致,亟需寻找有效的信息融合方法将内外场数据进行有效利用,以实现精准的液压泵寿命估计。本文提出失效物理与外场数据调制融合的寿命估计方法,通过构建混合润滑多场耦合液压泵失效物理模型,将其性能退化用随机过程描述;采集外场动态测试数据,用粒子滤波将动态外场数据调制更新到物理退化过程,基于最优重要性粒子采样消除外场观测数据的不确定性影响,通过正则变换重采样解决样本粒子枯竭问题,将失效物理与外场数据有机融合实现航空液压泵准确的寿命估计。试验结果表明本文提出的方法能够有效提高航空液压泵的寿命估计准确度。

关键词: 失效物理, 数据调制, 寿命估计, 粒子滤波, 航空液压泵

Abstract: The hydraulic pump is an important component of aircraft hydraulic system with long life, and most of operational time is in the actual flight. It is difficult to estimate its useful life only using in-situ test data. However, the real flight data of hydraulic pump have some uncertainties, and the load profiles imposed are different from those in manufactory. Therefore, it is urgent to find an effective method to integrate the manufactory and real flight data to achieve an accurate life estimation of the aircraft hydraulic pump. In this paper, a remaining useful life estimation method is proposed based on failure physics and real flight data. Considering the mixed lubrication condition and multi-field coupling, the physics-based degradation model of hydraulic pump is constructed, and the performance degradation is described as a stochastic process. After that, the real flight data are collected, and are processed by a particle filter to modulate the physics-based degradation model dynamically. To eliminate the uncertainty of real flight data, the optimal importance sampling and the regular granule resampling are used to overcome the particle degeneracy. Experimental results demonstrate that the proposed method can effectively improve the life estimation accuracy of aircraft hydraulic pump.

Key words: physics of failure, data driven, life prediction, particle filter, aircraft hydraulic pump

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