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On-line Bayesian model updating for structural health monitoring

机译：在线贝叶斯模型更新以进行结构健康监测

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摘要

Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach.
机译：疲劳引起的裂纹是一种危险的失效机制，会影响承受交替负载循环的机械组件。应该采用系统健康监测来识别可能危及结构的裂缝。由于不确定性的不同来源，实时损坏检测可能无法识别裂缝，而不确定性的来源尚未得到充分评估，甚至被完全忽略。在本文中，一种新颖有效且鲁棒的程序用于检测机械部件中的裂纹位置和长度。使用贝叶斯模型更新框架，该框架允许考虑不确定性的相关来源。支持该方法的想法是确定与实验测量结果最相符的裂纹。为了解决贝叶斯方法的计算成本，采用仿真器代替了计算成本高昂的有限元模型。为了提高该过程的整体鲁棒性，分析了不同的数值可能性，测量噪声和模型参数值的不精确性，并量化了其影响。讨论了随机更新的准确性和数值程序的效率。实验铝框架和典型的汽车悬架臂的数值模型用于证明该方法的适用性。

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