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

doi:10.7527/S1000-6893.2017.220925

固体力学与飞行器总体设计

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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

陈健, 袁慎芳, 王卉, 邱雷

南京航空航天大学航空宇航学院机械结构力学及控制国家重点实验室, 南京 210016

收稿日期:2016-11-10 修回日期:2017-09-08 出版日期:2017-11-15 发布日期:2017-09-08
通讯作者: 袁慎芳 E-mail:ysf@nuaa.edu.cn
基金资助:
国家自然科学基金重点项目（51635008）；国家自然科学基金（51575263）；国家自然科学基金杰出青年基金（51225502）；江苏省高校优势学科建设工程资助项目；青蓝工程；江苏省普通高校学术学位研究生创新计划项目（KYLX16_0333）

Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction

CHEN Jian, YUAN Shenfang, WANG Hui, QIU Lei

State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2016-11-10 Revised:2017-09-08 Online:2017-11-15 Published:2017-09-08
Supported by:
Key Program of National Natural Science Foundation of China (51635008); National Natural Science Foundation of China (51575263); National Natural Science Funds for Distinguished Young Scholars of China (51225502); Priority Academic Program Development of Jiangsu Higher Education Institutions of China; Qing Lan Project of Jiangsu Province of China; Funding of Jiangsu Innovation Program for Graduate Education (KYLX16_0333)

摘要/Abstract

摘要：

飞行器结构的疲劳裂纹扩展预测对保障结构安全、实现视情维护具有重要意义。结合粒子滤波算法和结构健康监测方法进行在线的疲劳裂纹扩展预测是近年来刚刚开始研究的新方法，该方法通过状态空间模型表征疲劳裂纹扩展过程中的不确定性，同时通过贝叶斯方法将结构健康监测所获取的结构实际裂纹观测值用于修正裂纹扩展模型的预测误差，实现更准确的疲劳裂纹扩展在线预测。由于该方法的研究刚刚开展，已有研究中粒子滤波算法的重要性密度函数往往简单选取为先验转移概率密度，存在严重的粒子退化问题。另一方面出于简单考虑，仅采用表征裂纹稳定扩展区的Paris模型。针对上述问题，本文提出一种基于高斯权值-混合建议分布粒子滤波的疲劳裂纹在线预测方法，基于表征裂纹全扩展区域的NASGRO裂纹扩展模型建立疲劳裂纹扩展状态方程，以主动Lamb波监测方法实现结构裂纹的在线监测，借助在线结构健康监测的优势，在粒子滤波时选取重要性密度函数为观测概率密度和先验转移概率密度的混合分布，同时基于先验估计获取高斯权值进行权值更新。本文进一步进行了仿真研究，结果表明所提出的方法优化了疲劳裂纹扩展预测的准确性。

关键词: 故障预测与健康管理, 结构健康监测, 裂纹扩展预测, 粒子滤波, NASGRO模型, 混合建议分布

Abstract:

Fatigue crack growth prediction is vital for ensuring structural safety and achieving condition-based maintenance of aircraft. Recently, novel methods for on-line fatigue crack growth prediction have drawn a lot of attention, which combine the particle filter algorithm with structural health monitoring. In these methods, a state space model is developed to represent uncertainties during fatigue crack growth, and the actual crack measurements obtained with structural health monitoring are incorporated to update the result derived from a fatigue crack growth model using Bayesian theorem. However, most of those methods employ the prior probability density function as the importance density in the particle filter algorithm, and thus suffer from the problem of severe particle degeneracy. In addition, the Paris law that can only describe the stable crack growth region is commonly used due to its simplicity. To overcome these problems, this paper proposes an on-line fatigue crack growth prediction method based on the Gaussian weighting-mixture proposal distribution particle filter. The NASGRO model which can describe the entire crack growth region is employed to establish the state equation of fatigue crack growth. The active Lamb wave based monitoring method is used for on-line structural crack monitoring. Based on on-line monitoring, the mixture proposal distribution fusing the prior probability density and the measurement probability density is adopted as the importance density function, and the Gaussian weighting process is proposed with the prior estimate of the crack length. Simulation data are utilized for validation, and the result shows the proposed method can significantly optimize the accuracy of fatigue crack growth prediction.

Key words: prognostics and health management, structural health monitoring, crack growth prediction, particle filter, NASGRO model, mixture proposal distribution

中图分类号:

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.

CHEN Jian, YUAN Shenfang, WANG Hui, QIU Lei. Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(11): 220925-220925.

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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

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

Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction

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