Noise filtering in the total focusing method by decomposition of the time reversal operator and the virtual array approach

Bannouf, Souad; Robert, Sébastien; Casula, O.; Prada, Claire

doi:10.1063/1.4789134

Cited by 7 publications

(10 citation statements)

References 6 publications

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“…TFM uses this matrix to create images that lend themselves to a relatively easy interpretation by both human and artificial intelligence. However, TFM images are often contaminated by noise and various strategies have been offered to modify the TFM algorithm to eliminate false indications [7,8] and reduce noise [8][9][10], enabling real-time imaging with portable NDT devices [8,11]. Researchers also began to explore application of machine learning to NDT [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.

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Section: Introductionmentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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“…This means that all the information exists to undertake subsequent processing of the data to generate an image. Total Focusing Method (TFM) is a technique of using the data from FMC to produce an image which is focused on every specified point in the image [2] . The algorithm using the maximum amount of information available for each point has the potential to generate ultrasonic images with better resolution and contrast [3] [4] .…”

Section: Introductionmentioning

confidence: 99%

Efficiency improvement of total focusing imaging based on sparse array method

Peng

et al. 2014

2014 IEEE Far East Forum on Nondestructive Evaluation/Testing

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In this paper, a design concept of sparse array is applied to total focusing imaging technique. During the process, the elements of a phased array are partially selected according to the theory of effective aperture, in order to reduce the amount of captured data and shorten the processing time of TFM imaging. Simulated imaging with Field II program and experimental verification on two standard phased array specimens are performed, based on three selected arrays which are sparsely as 1/2, 1/4 and 1/8. The results compared with the full array in terms of image quality and computing efficiency, show that the computing efficiency of TFM imaging can get a rise of 40 percent by adopting the sparse array design. Meanwhile, an approximate resolution can be achieved at the lowest array sparse rate of 1/4. Affected by the side-lobes of the ultrasonic beam, contrast of the image may suffer little loss.

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“…More and more applications [16], [17] and further researches [18], [19] covering kinds of NDT situations are based on TFM. These studies only need to focus on linear array data.…”

Section: Introductionmentioning

confidence: 99%

Efficient imaging techniques using Sub-aperture matrix capture and two-dimensional sparse array

Yang

Guo

Gao

et al. 2014

2014 IEEE Far East Forum on Nondestructive Evaluation/Testing

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Total focusing method (TFM) is a post-processing algorithm for ultrasonic imaging, usually used for processing linear array data. In this paper, TFM is extended to obtain three-dimensional (3D) image based on two-dimensional array data. However, it takes a long time to get 3D information from large amount of 2D array data by TFM. So Sub-aperture matrix capture (SMC) and sparse array are taken into account to solve this issue. The experimental results prove that TFM precisely reconstruct 3D information of defects through the optimized method. In a small angle imaging range, SMC can reach the same image quality and the same array performance indicator (API) as full matrix capture (FMC). In addition, it won't take more than one quarter of the time token by FMC for ultrasonic imaging. Although the spurious image is enhanced with the reducing sparseness, the processing time is reduced. Considering both image quality and real-time imaging, the imaging efficiency might be improved by 50 times through the combination of the two methods. The paper provides an optional approach to achieve real-time imaging system.

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Noise filtering in the total focusing method by decomposition of the time reversal operator and the virtual array approach

Cited by 7 publications

References 6 publications

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Efficiency improvement of total focusing imaging based on sparse array method

Efficient imaging techniques using Sub-aperture matrix capture and two-dimensional sparse array

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