Real-Time Prognosis of Crack Growth Evolution Using Sequential Monte Carlo Methods and Statistical Model Parameters
Real-Time Prognosis of Crack Growth Evolution Using Sequential Monte Carlo Methods and Statistical Model Parameters
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DOI：
10.1109/tr.2014.2366759
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发表时间：
2015-06
影响因子：
5.9
通讯作者：
Matteo Corbetta;C. Sbarufatti;A. Manes;M. Giglio
中科院分区：
文献类型：
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作者：
Matteo Corbetta;C. Sbarufatti;A. Manes;M. Giglio
本文提出了一种监测和预测疲劳裂纹扩展的概率方法。该方法利用序贯蒙特卡罗抽样和模型参数的概率密度函数相结合的方法。该技术建立了裂纹演化的自适应动态状态空间模型，能够识别最可能的参数，增强了对系统剩余寿命的估计。本文提出的寿命预测器可用于民用、工业和航空航天领域的关键结构的高级维修策略中。该算法首先被应用于模拟的裂纹扩展，然后被应用于直升机壁板上的一些实验室测试的实验裂纹扩展。最后讨论了该系统在在线连续监测系统中的适用性。
A probabilistic method to monitor and predict fatigue crack propagation is presented in this work. The technique makes use of sequential Monte Carlo sampling combined with the probability density functions of the model parameters. The technique leads to an adaptive dynamic state-space model for crack evolution able to identify the most probable parameters, enhancing the estimation of the residual life of the system. The lifetime predictor presented here could be implemented in advanced maintenance strategies for critical structures employed in civil, industrial, and aerospace fields. The algorithm is first applied to a simulated crack growth, and then to some experimental crack propagations from laboratory tests on helicopter panels. The applicability within on-line continuous monitoring systems is discussed at the end of the paper.