Improving the reliability of automated non-destructive inspection

Brierley, Nick; Tippetts, Trevor; Cawley, P.

doi:10.1063/1.4865057

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Indeed, this would be consistent with the actual mapping work of the ANNs systems, namely, classification and characterization, 138 and studies indicate that by applying a threshold to the probability value, it would actually be easier to assess the fusion results using the hit-miss rate 18 or false alarm criteria. 26,139 However, it appears the outputs of most of the studies in this area only provide Boolean values between 0 and 1 (i.e., flawed or nonflawed), whereas in real-time applications, detailed information about the structure (e.g., size, orientation, location of the flaw, etc.) is often needed.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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

Nsengiyumva

Zhong

Luo

et al. 2021

Structural Contr & Hlth

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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.data fusion, diagnostic and prognostic health monitoring (DPHM), multisensor fusion and integration (MFI), nondestructive evaluation (NDE), structural health monitoring (SHM), structural systems | INTRODUCTIONEngineering structures are manufactured through multivariable processes and can easily develop defects during the manufacturing process and suffer from various issues (e.g., inappropriate usages, progressive aging, impact damage, excessive loading, and natural hazards such as earthquakes, hurricanes, etc.) during their service life. 1,2 To this end, several nondestructive evaluation (NDE) techniques have been devised (e.g., ultrasonic testing, infrared thermography, terahertz spectrometry, and imaging) to inspect these structures and their corresponding components to ensure they are free from defects and that their structural integrity is maintained throughout their service life. [1][2][3][4] At the manufacturing stage, NDE determines the structure's fitness to accomplish its intended function, and during the service stage, it determines the structure's health status, monitors the damage mechanism,

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Section: Artificial Neural Networkmentioning

confidence: 99%