Investigating human factors in manual ultrasonic testing: testing the human factors model

Bertovic, Marija; Gaal, Mate; Müller, Christoph E.; Fahlbruch, Babette

doi:10.1784/insi.2011.53.12.673

Cited by 24 publications

(11 citation statements)

References 3 publications

(1 reference statement)

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“…requalification every 5 years in railway) and/or by a demonstration/practice task, which would give inspectors an opportunity to train their skills immediately before the inspection (also known as the "´just-in-time´ training"). The benefits of skill demonstration/practice task were observed in a study by Bertovic et al [16], in which the participants were given an opportunity to practice on a test piece to demonstrate whether they have understood the inspection procedure. The participants reported high satisfaction with the demonstration task, as it allowed them to refresh their skills and provided feedback about their performance according to a new inspection procedure.…”

Section: Human Factorsmentioning

confidence: 99%

Conclusions of the 6th European American Workshop on reliability of NDE

Mueller

Bertovic

Kanzler

et al. 2016

AIP Conference Proceedings

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Section: Human Factorsmentioning

confidence: 99%

Conclusions of the 6th European American Workshop on reliability of NDE

Mueller

Bertovic

Kanzler

et al. 2016

AIP Conference Proceedings

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“…Mueller et al (2012) note that the model elements introduced by Stephens Jr. (2000) are influence by a higher level organizational context. Bertovic et al (2010) present a reliability model built according to principles in the Socio-Technical Systems Approach, which assumes that a system is composed of technical as well as social or human elements. This model interprets organizational context as having overarching influence on all other essential parameters.…”

Section: Summary Human Factors Literaturementioning

confidence: 99%

Review of Literature for Model Assisted Probability of Detection

Meyer

Crawford

Lareau

et al. 2014

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This report documents a literature survey of so-called "Model Assisted Probability of Detection" (MAPOD) approaches that may be useful in determining the probability of detection (POD), a metric for quantifying the performance of nondestructive evaluation (NDE) methods. The objective of this report is to summarize MAPOD concepts that have been proposed to date in order to assess specific approaches that may be appropriate for application to improve estimates of POD for field NDE of nuclear power plant components. The limitations of laboratory-based studies to replicate actual field conditions are well-recognized and not limited to the nuclear power industry. Probability of detection estimates based on laboratory studies generally provide ideal environments for performing examinations as compared field settings, which typically will result in non-conservative estimates of POD for field applications. As a consequence of this effort, the authors conclude that use of MAPOD concepts to improve estimates of field NDE performance may require access to certain field data, or alternatively, may require significant laboratory studies to assess the influence of human and environmental shaping factors. If the necessary field data cannot be made available, it is proposed that laboratory efforts will have greater chance of success by focusing on a specific examination technique and component application. The authors also find that MAPOD concepts may have a more immediate contribution to the nuclear power industry through their use in extending personnel and procedure qualifications beyond established limits. Finally, another basis was identified that may be used to adjust POD curves generated in previous reliability studies and performance demonstrations. Many of the previous studies calculated POD as the average of performance data which may be inappropriate for many applications, as the calculation may be nonconservative. A more conservative approach to estimating POD would include basing the calculation on a lower statistical quantile of the data set. v

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“…Therefore, often authors investigate and develop new methods for determining ultrasonic velocity [7,8], but usually they present measurement results without the associated measurement uncertainty. Moreover, in [9], the author pointed out that, in ultrasonic inspections, the human factors of time pressure, mental workload, and experience influence the quality of the performance, which can bring about inaccurate inspection results. On the other hand, an electromagnetic (EM) wave has been involved in the thickness inspection of non-metallic materials due to its remarkable features.…”

Section: Introductionmentioning

confidence: 99%

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

Hien

Hong

2021

Applied Sciences

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Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

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Investigating human factors in manual ultrasonic testing: testing the human factors model

Cited by 24 publications

References 3 publications

Conclusions of the 6th European American Workshop on reliability of NDE

Conclusions of the 6th European American Workshop on reliability of NDE

Review of Literature for Model Assisted Probability of Detection

Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

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