Data registration for automated non-destructive inspection with multiple data sets

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

doi:10.1063/1.4789255

Cited by 2 publications

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“…In NDE data fusion, the data registration takes place at the preprocessing stage and it is particularly important when fusing data from multiple sources that require to be associated to a physical point on the test sample 44 or if the measuring sensors are not situated at the same position nor have identical resolutions 20 . In general, the concept of data registration does contain two different meanings, namely, the geometrical registration (i.e., the start and endpoints are defined) and the physical registration (i.e., data is associated to its corresponding physical position) 45 .…”

Section: Nde Data Fusion Levels Requirements and Sensor Integration A...mentioning

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: Nde Data Fusion Levels Requirements and Sensor Integration A...mentioning

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

View full text Add to dashboard Cite

show abstract

“…While there are parallels to medical imaging in this work, especially in terms of the dimensionality and potential multi-modality of the data [ 34 ], NDE test subject variability is low and the types of possible distortions limited compared with the human body [ 35 , 36 ]. The system developed and adopted by the authors is described in detail in [ 37 – 39 ]. Key features of this registration framework include a physical model of the data acquisition that attempts to describe all conceivable benign distortions of the data and the use of a multi-objective optimization [ 40 ], allowing alignment and hence positional uncertainty to be assessed, with consequences for the later data fusion.…”

Section: Data Fusion Contextmentioning

confidence: 99%

Data fusion for automated non-destructive inspection

2014

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In industrial non-destructive evaluation (NDE), it is increasingly common for data acquisition to be automated, driving a recent substantial increase in the availability of data. The collected data need to be analysed, typically necessitating the painstaking manual labour of a skilled operator. Moreover, in automated NDE a region of an inspected component is typically interrogated several times, be it within a single data channel due to multiple probe passes, across several channels acquired simultaneously or over the course of repeated inspections. The systematic combination of these diverse readings is recognized to offer an opportunity to improve the reliability of the inspection, but is not achievable in a manual analysis. This paper describes a data-fusion-based software framework providing a partial automation capability, allowing component regions to be declared defect-free to a very high probability while readily identifying defect indications, thereby optimizing the use of the operator's time. The system is designed to applicable to a wide range of automated NDE scenarios, but the processing is exemplified using the industrial ultrasonic immersion inspection of aerospace turbine discs. Results obtained for industrial datasets demonstrate an orders-of-magnitude reduction in false-call rates, for a given probability of detection, achievable using the developed software system.

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Data registration for automated non-destructive inspection with multiple data sets

Cited by 2 publications

References 0 publications

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

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

Data fusion for automated non-destructive inspection

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