Deep learning in automated ultrasonic NDE – Developments, axioms and opportunities

Cantero‐Chinchilla, Sergio; Wilcox, Paul D.; Croxford, Anthony J.

doi:10.1016/j.ndteint.2022.102703

Cited by 77 publications

(20 citation statements)

References 77 publications

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“…As a result of this, the human factor influences the quality and speed of the defectogram analysis. The small size of graphical images of defects, against the background of a significant length of the entire defectogram, measured in tens of kilometers of the railway track, significantly increases the psycho-emotional load on the flaw detector operator and increases the likelihood of missing defects [1,3,[7][8][9][10], which increases the risk of accidents in railway transport. Therefore, an important direction is the creation of automatic defect recognition and expert systems.…”

Section: Decoding Of Defectogramsmentioning

confidence: 99%

Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics

Kaliuzhnyi

2023

Artificial Intelligence Evolution

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The task of searching for defects on defectograms of ultrasonic inspection of rails using machine learning methods is considered difficult due to the lack of a sufficient representative training data set. In this study, on the example of defects in bolt holes of rails, the possibility of synthesizing an effective classifier trained on artificial data obtained by mathematical modeling is shown. Experiments have shown a high accuracy of predicting samples of real (not model) data exceeding 99%.

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Section: Decoding Of Defectogramsmentioning

confidence: 99%

Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics

Kaliuzhnyi

2023

Artificial Intelligence Evolution

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“…On the other hand, the evaluation is done automatically. This enables new ML-based NDT systems that match the demands of Industry 4.0, such as mitigating the influence of human factors and reducing the time needed for evaluation of the data [5]. In the future, the NDT inspector could be supported or fully replaced by these kinds of systems.…”

Section: Related Workmentioning

confidence: 99%

“…For ultrasound testing, the studies focus either on time series data (A-scans [6][7][8]), or on image datasets (B-scans [9] or C-scans [7]). Furthermore, according to [5] there are different tasks in ultrasound testing which are solved with deep learning. These are for example denoising, defect detection, data reduction, improving the resolution, defect characterization and material property determination.…”

Section: Related Workmentioning

confidence: 99%

Spatial and Temporal Deep Learning in Air-coupled Ultrasonic Testing for Enabling NDE 4.0

Schmid

Dürrmeier

Große

2022

Preprint

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Air-coupled ultrasonic (ACU) testing has been used for several years to detect defects in plate-like structures. Especially, for automated testing procedures, ACU testing is advantageous in comparison to conventional testing. However, the evaluation of the measurement data is usually done in a manual manner, which is an obstruction to the application of ACU testing. The goal of this study is to automate and improve defect characterization and NDE 4.0 accordingly with deep learning. In conventional ACU testing the measurement data contains temporal (A-scans) and spatial (C-scans) information. Both data types are investigated in this study. For the A-scans, which represent time series data, neural networkarchitectures tailored to such data types are applied. In addition, it is evaluated if further adaptions of the training procedure increase the performance. The C-scans are segmented by applying differentU-net similar architectures and training strategies. In order to use spatial and temporal information, a further approach is taken. The prediction of the time series models is segmented with imagemodels. To the performance of all trained models and training strategies is compared with the F1-score. As specimens, artificial defects in acrylic and carbon fiber-reinforced polymers plates are investigated.

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“…Over the last decade, artificial intelligence (AI) has been increasingly used in NDT methods [ 5 ]. For example, analyzing ultrasonic signals obtained through NDT tests using machine learning (ML) [ 6 ] and deep learning (DL) [ 7 ] methods. Shrifan et al [ 8 ] reviewed various NDT techniques for their suitability of applying AI approaches and providing a detailed analysis of AI used for microwave NDT compared to other conventional NDT methods.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

126

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This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

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Deep learning in automated ultrasonic NDE – Developments, axioms and opportunities

Cited by 77 publications

References 77 publications

Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics

Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics

Spatial and Temporal Deep Learning in Air-coupled Ultrasonic Testing for Enabling NDE 4.0

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

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