失效物理与数据调制融合的航空液压泵寿命估计

doi:10.7527/S1000-6893.2022.27347

固体力学与飞行器总体设计

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

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. 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 Shaoping^1,2, GENG Yixuan¹, SHI Cun¹

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/Abstract

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

中图分类号:

V240.2

王少萍, 耿艺璇, 石存. 失效物理与数据调制融合的航空液压泵寿命估计[J]. 航空学报, 2022, 43(10): 527347-527347.

WANG Shaoping, GENG Yixuan, SHI Cun. Life estimation of aircraft hydraulic pump based on failure physics and data driven[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(10): 527347-527347.

参考文献

[1] 马纪明, 阮凌燕, 付永领, 等. 航空液压泵加速寿命试验现状及方法研究(连载2) 航空液压泵典型失效模式及加速方法[J]. 液压与气动, 2015(7):1-6. MA J M, RUAN L Y, FU Y L, et al. Typical failure modes and accelerated lifetime test methods for aircraft hydraulic pump[J]. Chinese Hydraulics & Pneumatics, 2015(7):1-6 (in Chinese).
[2] PECHT M, JAAI R. A prognostics and health management roadmap for information and electronics-rich systems[J]. Microelectronics Reliability, 2010, 50(3):317-323.
[3] GEBRAEEL N, ELWANY A, PAN J. Residual life predictions in the absence of prior degradation knowledge[J]. IEEE Transactions on Reliability, 2009, 58(1):106-117.
[4] QIU J, SETH B B, LIANG S Y, et al. Damage mechanics approach for bearing lifetime prognostics[J]. Mechanical Systems and Signal Processing, 2002, 16(5):817-829.
[5] SINGLETON R K, STRANGAS E G, AVIYENTE S. Extended Kalman filtering for remaining-useful-life estimation of bearings[J]. IEEE Transactions on Industrial Electronics, 2015, 62(3):1781-1790.
[6] 张孟迪. 基于PSP模型的40 nm MOS器件HCI可靠性模型研究[D]. 上海:华东师范大学, 2012:33-45. ZHANG M D. The research of PSP-dependent HCI reliability model for 40 nm MOS[D]. Shanghai:East China Normal University, 2012:33-45 (in Chinese).
[7] 郑德强, 李海波, 张正平, 等. 感应电动机寿命预测的加速退化试验方法研究[J]. 宇航学报, 2011, 32(10):2280-2284. ZHENG D Q, LI H B, ZHANG Z P, et al. Study on accelerated degradation testing for predicting the life of induction motors[J]. Journal of Astronautics, 2011, 32(10):2280-2284 (in Chinese).
[8] 李玲玲, 顾训华, 李凤强, 等. 基于GaAs激光器性能退化的可靠性度量方法[J]. 工程设计学报, 2012, 19(3):166-169. LI L L, GU X H, LI F Q, et al. Reliability assessment method based on GaAs laser performance degradation[J]. Chinese Journal of Engineering Design, 2012, 19(3):166-169 (in Chinese).
[9] 肖承地, 刘春军, 刘卫东, 等. 基于加速性能退化的LED灯具可靠性评估[J]. 发光学报, 2014, 35(9):1143-1151. XIAO C D, LIU C J, LIU W D, et al. Reliability assessment of LED lamp based on acceleration degradation test[J]. Chinese Journal of Luminescence, 2014, 35(9):1143-1151 (in Chinese).
[10] 陈奇. 大功率LED封装热可靠性与寿命预测研究[D]. 武汉:华中科技大学, 2018:98-122. CHEN Q. Research on thermal reliability and lifetime prediction of high power LED packaging[D]. Wuhan:Huazhong University of Science and Technology, 2018:98-122 (in Chinese).
[11] 何怡刚,白月皎,鲁力. 基于DE-QPSO算法的MKRVM对电容式RF-MEMS开关的寿命预测方法[J].电子测量与仪器学报, 2020, 34(12):66-75. HE Y G, BAI Y J, LU L. MKRVM prediction of capacitive RF-MEMS switching life based on DE-QPSO algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(12):66-75 (in Chinese).
[12] 申彦斌, 张小丽, 夏勇, 等. Bi-LSTM神经网络用于轴承剩余使用寿命预测研究[J]. 振动工程学报, 2021, 34(2):411-420. SHEN Y B, ZHANG X L, XIA Y, et al. Bi-LSTM neural network for remaining useful life prediction of bearings[J]. Journal of Vibration Engineering, 2021, 34(2):411-420 (in Chinese).
[13] 刘斌, 徐靖, 霍美玲, 等. 基于多尺度自适应注意力网络的剩余寿命预测[J/OL]. 航空学报, (2022-04-07)[2022-05-24]. https://kns.cnki.net/kcms/detail/11.1929.V20220406.1727.008.html. LIU B, XU J, HUO M L, et al. Remaining useful life prediction based on multi-scale adaptive attention network[J/OL]. Acta Aeronautica et Astronautica Sinica, (2022-04-07)[2022-05-24]. https://kns.cnki.net/kcms/detail/11.1929.V20220406.1727.008.html (in Chinese).
[14] CUI L R, HUANG J B, LI Y. Degradation models with Wiener diffusion processes under calibrations[J]. IEEE Transactions on Reliability, 2016, 65(2):613-623.
[15] ZHANG J S, JIANG Y C, LI X, et al. An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty[J]. Reliability Engineering & System Safety, 2022, 222:108357.
[16] 王玺, 胡昌华, 任子强, 等. 基于非线性Wiener过程的航空发动机性能衰减建模与剩余寿命预测[J]. 航空学报, 2020, 41(2):223291. WANG X, HU C H, REN Z Q, et al. Performance degradation modeling and remaining useful life prediction for aero-engine based on nonlinear Wiener process[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(2):223291 (in Chinese).
[17] 夏爽. 基于维纳过程的机电装备性能退化建模与健康状态评估[D]. 武汉:华中科技大学, 2016:27-32. XIA S. Performance degradation modeling and health assessment for mechanical and electrical equipment based on Wiener process[D]. Wuhan:Huazhong University of Science and Technology, 2016:27-32 (in Chinese).
[18] KONG D J, QIN C W, HE Y, et al. Sensor-based calibrations to improve reliability of systems subject to multiple dependent competing failure processes[J]. Reliability Engineering & System Safety, 2017, 160:101-113.
[19] ZHANG C H, LU X, TAN Y Y, et al. Reliability demonstration methodology for products with Gamma Process by optimal accelerated degradation testing[J]. Reliability Engineering & System Safety, 2015, 142:369-377.
[20] TANG S J, YU C Q, WANG X, et al. Remaining useful life prediction of lithium-ion batteries based on the Wiener process with measurement error[J]. Energies, 2014, 7(2):520-547.
[21] 孙宏达, 景博, 焦晓璇,等.失效物理与数据驱动融合的燃油泵在线寿命预测[J/OL].仪器仪表学报, (2022-01-29)[2022-04-14].https://kns.cnki.net/kcms/detail/11.2179.th.20220128.1440.032.html. SUN H D, JING B, JIAO X X, et al. Online life prediction of the fuel pump based on failure physics and data-driven fusion[J/OL]. Chinese Journal of Scientific Instrument, (2022-01-29)[2022-04-14]. https://kns.cnki.net/kcms/detail/11.2179.th.20220128.1440.032.html (in Chinese).
[22] 范立明, 王崑声, 钱诚. 故障物理与粒子滤波融合的锂电池寿命预测方法[J]. 兵器装备工程学报, 2020, 41(9):171-175. FAN L M, WANG K S, QIAN C. Remaining useful life prediction of lithium battery based on physics of failure and particle filtering[J]. Journal of Ordnance Equipment Engineering, 2020, 41(9):171-175 (in Chinese).
[23] CHEN C C, PECHT M. Prognostics of lithium-ion batteries using model-based and data-driven methods[C]//Proceedings of the IEEE 2012 Prognostics and System Health Management Conference. Piscataway:IEEE Press, 2012:1-6.
[24] ZHAO F Q, TIAN Z G, ZENG Y. Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method[J]. IEEE Transactions on Reliability, 2013, 62(1):146-159.
[25] ZANG Y, SHANGGUAN W, CAI B G, et al. Hybrid remaining useful life prediction method. A case study on railway D-cables[J]. Reliability Engineering & System Safety, 2021, 213:107746.
[26] MA J M, CHEN J, LI J, et al. Wear analysis of swash plate/slipper pair of axis piston hydraulic pump[J]. Tribology International, 2015, 90:467-472.
[27] ORCHARD M, WU B Q, VACHTSEVANOS G. A particle filtering framework for failure prognosis[C]//Proceedings of World Tribology Congress III, 2008:883-884.
[28] 鲁峰, 黄金泉, 吕怡秋, 等. 基于非线性自适应滤波的发动机气路部件健康诊断方法[J]. 航空学报, 2013, 34(11):2529-2538. LU F, HUANG J Q, LV Y Q, et al. Aircraft engine gas-path components health diagnosis based on nonlinear adaptive filters[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(11):2529-2538 (in Chinese).
[29] 刘君强, 胡东斌, 潘春露, 等. 基于超统计的多阶段航空发动机剩余寿命预测[J]. 北京航空航天大学学报, 2021, 47(1):56-64. LIU J Q, HU D B, PAN C L, et al. Remaining useful life prediction of multi-stage aero-engine based on super statistics[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1):56-64 (in Chinese).
[30] LI S, FANG H J, SHI B. Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression[J]. Reliability Engineering & System Safety, 2021, 210:107542.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

失效物理与数据调制融合的航空液压泵寿命估计

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	岳敬轩, 王红茹, 朱东琴, ALEKSANDR Chupalov. 基于改进粒子滤波的无人机编队协同导航算法[J]. 航空学报, 2023, 44(14): 327995-327995.
[2]	王小涛, 张家友, 王邢波, 韩亮亮. 基于FastSLAM的绳系机器人同时定位与地图构建算法[J]. 航空学报, 2021, 42(1): 523893-523893.
[3]	关翔中, 蔡晨晓, 翟文华, 王磊, 邵鹏. 基于神经网络补偿的室内无人机组合导航系统[J]. 航空学报, 2020, 41(S1): 723790-723790.
[4]	陈健, 袁慎芳, 王卉, 邱雷. 基于高斯权值-混合建议分布粒子滤波的疲劳裂纹扩展预测[J]. 航空学报, 2017, 38(11): 220925-220925.
[5]	李鹏飞, 徐国栋, 董立珉, 候天蕊. X射线脉冲星信号时延的实时估计方法[J]. 航空学报, 2014, 35(7): 1966-1976.
[6]	李海君, 赵国荣, 黄婧丽, 周大旺. 一种基于NGA-QPF的航天器姿态估计方法[J]. 航空学报, 2014, 35(6): 1694-1702.
[7]	瑚成祥, 刘贵喜, 董亮, 王明, 张菁超. 区域杂波估计的多目标跟踪方法[J]. 航空学报, 2014, 35(4): 1091-1101.
[8]	袁慎芳, 张华, 邱雷, 杨伟博. 基于粒子滤波算法的疲劳裂纹扩展预测方法[J]. 航空学报, 2013, 34(12): 2740-2747.
[9]	郭子伟, 缪玲娟, 赵洪松, 沈军. 一种改进的类高斯和粒子滤波在大失准角传递对准中的应用[J]. 航空学报, 2013, 34(1): 164-172.
[10]	徐宇亮, 孙际哲, 陈西宏, 王光明. 基于加速退化试验和粒子滤波的电子设备故障预测方法[J]. 航空学报, 2012, 33(8): 1483-1490.
[11]	罗少华, 徐晖, 徐洋, 安玮. 改进的MMPHD机动目标跟踪方法[J]. 航空学报, 2012, 33(7): 1296-1304.
[12]	孙瑾, 顾宏斌, 侯建波, . 基于梯度方向二值模式特征的飞机翼尖跟踪技术[J]. 航空学报, 2011, 32(11): 2062-2072.
[13]	叶继坤;雷虎民;李炯;陈治湘. 基于普通分离原理的制导/估计综合设计方法[J]. 航空学报, 2011, 32(1): 137-144.
[14]	李峰荣;刘贵喜;孙庆方. 基于混合滤波的无线传感器网络融合跟踪方法[J]. 航空学报, 2010, 31(9): 1849-1857.
[15]	刘芳;冯永新. 一种基于TDDM裂谱信号的解模糊位搜索法[J]. 航空学报, 2010, 31(4): 738-743.