Chen Jian, Yuan Shenfang, Qiu Lei, Wang Hui, Yang Weibo
Research Center of Structural Health Monitoring and Prognosis, The State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China.
Research Center of Structural Health Monitoring and Prognosis, The State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China.
Ultrasonics. 2018 Jan;82:134-144. doi: 10.1016/j.ultras.2017.07.016. Epub 2017 Jul 25.
Accurate on-line prognosis of fatigue crack propagation is of great meaning for prognostics and health management (PHM) technologies to ensure structural integrity, which is a challenging task because of uncertainties which arise from sources such as intrinsic material properties, loading, and environmental factors. The particle filter algorithm has been proved to be a powerful tool to deal with prognostic problems those are affected by uncertainties. However, most studies adopted the basic particle filter algorithm, which uses the transition probability density function as the importance density and may suffer from serious particle degeneracy problem. This paper proposes an on-line fatigue crack propagation prognosis method based on a novel Gaussian weight-mixture proposal particle filter and the active guided wave based on-line crack monitoring. Based on the on-line crack measurement, the mixture of the measurement probability density function and the transition probability density function is proposed to be the importance density. In addition, an on-line dynamic update procedure is proposed to adjust the parameter of the state equation. The proposed method is verified on the fatigue test of attachment lugs which are a kind of important joint components in aircraft structures.
疲劳裂纹扩展的精确在线预测对于确保结构完整性的故障预测与健康管理（PHM）技术具有重要意义，由于诸如材料固有特性、载荷和环境因素等来源产生的不确定性，这是一项具有挑战性的任务。粒子滤波算法已被证明是处理受不确定性影响的预测问题的有力工具。然而，大多数研究采用基本粒子滤波算法，该算法使用转移概率密度函数作为重要性密度，可能会遭受严重的粒子退化问题。本文提出了一种基于新型高斯权重混合提议粒子滤波和基于主动导波的在线裂纹监测的疲劳裂纹扩展在线预测方法。基于在线裂纹测量，提出将测量概率密度函数和转移概率密度函数的混合作为重要性密度。此外，还提出了一种在线动态更新程序来调整状态方程的参数。所提出的方法在连接耳片的疲劳试验中得到了验证，连接耳片是飞机结构中一种重要的接头部件。
