Applied Artificial Intelligence in NDE

Osman, Ahmad; Duan, Yuxia; Kaftandjian, Valérie

doi:10.1007/978-3-030-48200-8_49-1

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…NDE techniques require high level of intelligence and discernment in performing the experiments and interpreting the results. Artificial Intelligence (AI) which is the intelligence demonstrated by machines to do tasks is a well-suited tool for NDE applications [9,10]. Major elements which drive the widespread application of AI algorithms include: broader development and availability of algorithms which some of them are open source and easy to use, availability of large sets of data for training, development and advancement of computational devices and their capabilities, and strong interest in new technologies such as smart manufacturing, autonomous devices and automated data processing [11].…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Taheri

Bocanegra

Taheri

2022

Sensors

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Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part’s integrity or causing damage to the component being tested. This includes monitoring materials’ condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

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Section: Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Taheri

Bocanegra

Taheri

2022

Sensors

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“…Achieving more accurate, time-and cost-efficient solutions can be endorsed by automating repetitive tasks during analysis. More than that, state-of-the-art computational techniques can be used and explored to advance the identification and interpretation of patterns in NDT test results [16,17].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis to Enhance Drone-Based Multi-Modal Inspection of Structures

Nooralishahi

Ramos

Pozzer

et al. 2022

Drones

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The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities.

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Reliability Evaluation of Testing Systems and Their Connection to NDE 4.0

Kanzler¹,

Rentala²

2021

Handbook of Nondestructive Evaluation 4.0

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Applied Artificial Intelligence in NDE

Cited by 4 publications

References 51 publications

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

Texture Analysis to Enhance Drone-Based Multi-Modal Inspection of Structures

Reliability Evaluation of Testing Systems and Their Connection to NDE 4.0

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