Remaining useful life prediction based on UKF for aircraft structure with fatigue crack

LUO Bin; LIN Lin; ZHONG Shisheng

doi:10.11918/j.issn.0367-6234.201709020

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Remaining useful life prediction based on UKF for aircraft structure with fatigue crack

更新时间：2026-10-23

- Remaining useful life prediction based on UKF for aircraft structure with fatigue crack
- Vol. 50, Issue 7, Pages: 38-45(2018)
- 作者机构：
  
  哈尔滨工业大学机电工程学院,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：10.11918/j.issn.0367-6234.201709020
  CLC： TU375.2
- Published：2018
- 稿件说明：
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LUO Bin, LIN Lin, ZHONG Shisheng. Remaining useful life prediction based on UKF for aircraft structure with fatigue crack[J]. 2018, 50(7): 38-45.
DOI：

LUO Bin, LIN Lin, ZHONG Shisheng. Remaining useful life prediction based on UKF for aircraft structure with fatigue crack[J]. 2018, 50(7): 38-45. DOI： 10.11918/j.issn.0367-6234.201709020.

摘要

针对机械系统中疲劳结构的剩余寿命(RUL)预测问题，提出了一种结合无迹卡尔曼滤波算法(UKF)的RUL预测方法.该方法包括疲劳裂纹性能参数评估和RUL预测两个部分.在性能参数评估部分，通过对Paris疲劳裂纹扩展公式进行离散化，建立了参数状态空间评估模型，并利用传感器获得的实时状态信息结合UKF算法对状态空间评估模型中的疲劳性能参数(C和m)以及疲劳裂纹长度表现出的不确定性进行评估，以避免状态信息不完备、工况噪声等不确定因素对结构疲劳寿命预测的影响；在剩余寿命预测部分，利用UKF算法评估得到的参数结果，结合离散化得到的递推裂纹扩展模型，对结构的剩余寿命进行预测.仿真结果表明：提出的方法能够很好地处理疲劳裂纹扩展模型中疲劳性能参数的不确定性，且在剩余寿命预测上，通过与扩展卡尔曼滤波算法(EKF)进行比较分析，发现所提方法能够更准确地预测结构疲劳裂纹的RUL.将离散的Paris疲劳裂纹扩展公式和UKF算法进行结合，能够有效地提高疲劳结构的剩余寿命预测精度.

Abstract

A novel remaining useful life(RUL)prediction method based on unscented Kalman filter(UKF)is proposed for structure with fatigue crack in machinery systems

which mainly includes two parts: performance evaluation of fatigue crack and RUL prediction. In the first part

a discrete state-space model is established based on the Paris law. Then the UKF is applied to estimate the two unknown Paris' law constants C and m combining with the real-time information obtained by sensors

in order to alleviate the negative influence on prediction accuracy caused by the uncertainty of incompletion of status information

as well as environmental noise. In the second part

the RUL of fatigue structure is predicted based on the discrete crack growth model according to the estimated result obtained by the UKF. The numerical experiments indicate that the UKF accurately identified the unknown parameters

furthermore

better performance in RUL prediction is obtained by comparing with extended Kalman filter(EKF)method. The RUL prediction accuracy can be efficiently improved by combining the discrete Paris law with UKF.

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