In recent years, the demand for reliable and accurate nondestructive evaluation (NDE) of structural systems has been growing and several nondestructive testing (NDT) techniques are being combined to address the issues of uncertainty in the testing results. However, the synergistic combination of multiple NDT techniques and the interpretation of the testing results require the ability to process and analyze data from several sources, a process that is referred to as “data fusion.” The latter has received considerable attention since the beginning of the 21st century, thanks to the advances in sensor technologies and data acquisition systems. Also, the new era of “Big Data” where heterogeneous data are measured by different sensors and processed for a unified purpose is another factor contributing to this rapid development and the establishment of new NDT opportunities. This article reviews the recent studies involving NDE data fusion for the inspection of structures and examines the state-of-the-art mathematical expressions used in different fusion algorithms. Challenges in the handling of the NDE data and the application of fusion algorithms are also identified and discussed. A generic framework of applying NDE data fusion is described, and a roadmap for potential research prospects in NDE data fusion is provided.

中文翻译：

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对用于结构系统检查的最先进 NDE 数据融合技术的重要见解

近年来，对结构系统可靠和准确的无损评估 (NDE) 的需求不断增长，并且正在结合多种无损检测 (NDT) 技术来解决测试结果中的不确定性问题。然而，多种无损检测技术的协同组合和测试结果的解释需要能够处理和分析来自多个来源的数据，这一过程被称为“数据融合”。由于传感器技术和数据采集系统的进步，后者自 21 世纪初以来受到了相当大的关注。还，“大数据”的新时代，异构数据由不同的传感器测量并为统一目的进行处理，是促成这种快速发展和建立新的无损检测机会的另一个因素。本文回顾了最近涉及 NDE 数据融合用于结构检查的研究，并检查了不同融合算法中使用的最新数学表达式。还确定并讨论了处理 NDE 数据和应用融合算法方面的挑战。描述了应用无损检测数据融合的通用框架，并提供了无损检测数据融合潜在研究前景的路线图。本文回顾了最近涉及 NDE 数据融合用于结构检查的研究，并检查了不同融合算法中使用的最新数学表达式。还确定并讨论了处理 NDE 数据和应用融合算法方面的挑战。描述了应用无损检测数据融合的通用框架，并提供了无损检测数据融合潜在研究前景的路线图。本文回顾了最近涉及 NDE 数据融合用于结构检查的研究，并检查了不同融合算法中使用的最新数学表达式。还确定并讨论了处理 NDE 数据和应用融合算法方面的挑战。描述了应用无损检测数据融合的通用框架，并提供了无损检测数据融合潜在研究前景的路线图。