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Modal acoustic emission for composite structures health monitoring: Issues to save computing time and algorithmic implementation

机译:用于复合结构健康监测的模态声发射:节省计算时间和算法实现的问题

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AbstractThis paper deals with assessing the integrity of composite structures via modal acoustic emission (MAE) technique. It is a continuity of the paper “On the modal acoustic emission of composite structures”, published last year by the authors in the same journal. To improve the reliability level of this technique (as it is the case of the other nondestructive techniques), exploring various innovative processing techniques of the collected data is required. Unfortunately, this task undergoes a huge volume of data, where its management (processing, reuse, etc.) should be achieved as well as possible. Hence, performing an efficient processing of the large data sets that can be generated via MAE, during the composite structures health monitoring, is a challenging topic. This study concerns the development of an algorithmic tool for resolving the problem of memory saturation, which can be encountered when working with such large data sets. It is a first step towards Big Data based solutions, launched recently by the authors’ team. A case of study is discussed, showing the robustness of the already implemented algorithmic tool.
机译: 摘要 本文旨在通过模态声发射(MAE)技术评估复合结构的完整性。这是作者去年在同一期刊上发表的“关于复合结构的模态声发射”论文的延续。为了提高该技术的可靠性水平(就像其他非破坏性技术一样),需要探索各种创新的采集数据处理技术。不幸的是,此任务需要处理大量数据,因此应尽可能实现其管理(处理,重用等)。因此,在复合结构健康状况监视期间,对可通过MAE生成的大数据集进行有效处理是一个具有挑战性的主题。这项研究涉及用于解决内存饱和问题的算法工具的开发,当使用如此大的数据集时可能会遇到这种问题。这是作者团队最近推出的基于大数据解决方案的第一步。讨论了一个研究案例,显示了已经实现的算法工具的鲁棒性。

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