Human factors in non-destructive testing (NDT): risks and challenges of mechanised NDT

Bertovic, Marija

doi:10.14279/depositonce-4685

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…The main difference between SHM and NDE regarding influencing factors relies on the degree of automatization and of structure-system integration. On the one hand, human and organizational factors represent a major influence in NDE due to manual inspection [ 37 ]. On the other hand, the algorithms and intrinsic factors gain importance with SHM due to its high automatization and integration in the monitored structure.…”

Section: Need For Performance Assessment In Shmmentioning

confidence: 99%

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

Mueller

Memmolo

Tschöke

et al. 2022

Sensors

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To assess the ability of structural health monitoring (SHM) systems, a variety of prerequisites and contributing factors have to be taken into account. Within this publication, this variety is analyzed for actively introduced guided wave-based SHM systems. For these systems, it is not possible to analyze their performance without taking into account their structure and their applied system parameters. Therefore, interdependencies of performance assessment are displayed in an SHM pyramid based on the structure and its monitoring requirements. Factors influencing the quality, capability and reliability of the monitoring system are given and put into relation with state-of-the-art performance analysis in a non-destructive evaluation. While some aspects are similar and can be treated in similar ways, others, such as location, environmental condition and structural dependency, demand novel solutions. Using an open-access data set from the Open Guided Waves platform, a detailed method description and analysis of path-based performance assessment is presented.The adopted approach clearly begs the question about the decision framework, as the threshold affects the reliability of the system. In addition, the findings show the effect of the propagation path according to the damage position. Indeed, the distance of damage directly affects the system performance. Otherwise, the propagation direction does not alter the potentiality of the detection approach despite the anisotropy of composites. Nonetheless, the finite waveguide makes it necessary to look at the whole paths, as singular phenomena associated with the reflections may appear. Numerical investigation helps to clarify the centrality of wave mechanics and the necessity to take sensor position into account as an influencing factor. Starting from the findings achieved, all the issues are discussed, and potential future steps are outlined.

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Section: Need For Performance Assessment In Shmmentioning

confidence: 99%

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

Mueller

Memmolo

Tschöke

et al. 2022

Sensors

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“…The traditional techniques that are usually employed in the area of non-destructive testing (NDT) to categorize flaws are prone to subjective influences, such as a person's opinion [39]. In addition, since the received signals are one-dimensional, it is difficult to use them to precisely detect diversions by visual inspection.…”

Section: Dnn Architecturesmentioning

confidence: 99%

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning

Zayat

Obeed

Chaaban

2022

Sensors

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In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff–Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.

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“…With the increasing proliferation and adoption of process automation in the manufacturing industry, high manufacturing throughput of components is achievable [8][9][10][11]. The need to inspect these fabricated components places a huge demand on NDT technicians [12], who traditionally employ trained skills and experience to manipulate X-ray acquisition setups, and visually assess and evaluate NDT radiography images. This evolving scenario birthed the need for NDT radiography to adopt non-conventional approaches in performing radiographic inspections [13], to ameliorate the shortage in the number of qualified NDT radiography personnel available to carry out such arduous inspection tasks, and reduce the occurrence of human error [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

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Human factors in non-destructive testing (NDT): risks and challenges of mechanised NDT

Cited by 9 publications

References 38 publications

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

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