Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

• Published in: Structural control and health monitoring Vol. 29; no. 1
• Main Authors: Nsengiyumva, Walter, Zhong, Shuncong, Luo, Manting, Zhang, Qiukun, Lin, Jiewen
• Format: Journal Article
• Language: English
• Published: Pavia Wiley Subscription Services, Inc 01.01.2022
• Subjects: Algorithms; Big Data; Data acquisition; data fusion; Data integration; diagnostic and prognostic health monitoring (DPHM); Inspection; multisensor fusion and integration (MFI); nondestructive evaluation (NDE); Nondestructive testing; structural health monitoring (SHM); structural systems
• ISSN: 1545-2255; 1545-2263
• DOI: 10.1002/stc.2857

Summary 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.