In-situ fatigue life prognosis for composite laminates based on stiffness degradation
In-situ fatigue life prognosis for composite laminates based on stiffness degradation
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DOI：
10.1016/j.compstruct.2015.05.006
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发表时间：
2015-11-15
影响因子：
6.3
通讯作者：
Goebel, Kai
中科院分区：
文献类型：
--
作者：
Peng, Tishun;Liu, Yongming;Goebel, Kai
提出了一种实时复合材料疲劳寿命预测框架。所提出的方法结合贝叶斯推理，压电传感器测量，和机械刚度退化模型在原位疲劳寿命预测。首先，将复合材料刚度退化引入到循环载荷下复合材料疲劳损伤累积中，提出了一种新的基于增长率的刚度退化模型。在此基础上，讨论了基于贝叶斯更新的疲劳寿命预测方法。几个来源的不确定性和发达国家的刚度退化模型包括在预测框架。其次，设计并进行了压电传感器原位复合材料疲劳试验，采集传感器信号和整体刚度数据。信号处理技术的实施，以提取损伤诊断功能。检测到的刚度退化集成在贝叶斯推理框架的剩余使用寿命（RUL）预测。预测性能的实验数据进行了验证，使用性能指标。最后，对本文的研究工作进行了总结，并提出了进一步的研究方向. (C)2015爱思唯尔有限公司版权所有。
In this paper, a real-time composite fatigue life prognosis framework is proposed. The proposed methodology combines Bayesian inference, piezoelectric sensor measurements, and a mechanical stiffness degradation model for in-situ fatigue life prediction. First, the composites stiffness degradation is introduced to account for the composites fatigue damage accumulation under cyclic loadings and a new growth rate-based stiffness degradation model is developed. Following this, the general Bayesian updating-based fatigue life prediction method is discussed. Several sources of uncertainties and the developed stiffness degradation model are included in the prognosis framework. Next, an in-situ composites fatigue testing with piezoelectric sensors is designed and performed to collected sensor signal and the global stiffness data. Signal processing techniques are implemented to extract damage diagnosis features. The detected stiffness degradation is integrated in the Bayesian inference framework for the remaining useful life (RUL) prediction. Prognosis performance on experimental data is validated using prognostics metric. Finally, some conclusions and future work are drawn based on the proposed study. (C) 2015 Elsevier Ltd. All rights reserved.