The human factors approach relies on understanding the properties of human capability and limitations under various conditions and the application of that knowledge in designing and developing safe systems. Following the principles of the MTO (Man Technology Organisation) approach, emphasis should be given to the way people interact with technical as well as organisational systems. A model describing human factor influences in relation to the performance shaping factors and their effect on manual ultrasonic inspection performance had been built and a part of it empirically tested. The experimental task involved repeated inspection of 18 defects according to the standard procedure under no, middle and high time pressure. Stress coping strategies, the mental workload of the task, stress reaction and organisational factors have been measured. The results have shown that time pressure, mental workload and experience influence the quality of the inspection performance. Organisational factors and their influence on the inspection results were rated as important by the operators. However, further research is necessary into the effects of stress.

The aim of this publication is to provide an overview of new methodologies for evaluating the reliability of NDE systems in accordance with the specific requirements of industrial applications. After a review of the substantive issues of the previous decades, the go forward guidance is concluded. For high safety demands a quantitative POD (Probability of Detection) created from hit/miss experiments or signal response analysis and ROC (Receiver Operating Characteristics) are typically created. The modular reliability model distinguishes between the pure physical-technical influence, industrial application factors, and human factors. It helps to learn which factors can be determined by modeling and by open or blind trials. A new paradigm is offered to consider the POD or reliability of the system as a function of the configuration of input variables and is used for optimization rather than for a final judgement. New approaches are considered dealing with real defects in a realistic environment, affordable but precisely like the Bayesian approach or model assisted methods. Among the influencing parameters, human factors are of high importance. A systematic psychological approach helps to find out where the bottlenecks are and shows possibilities for improvement.

Abstract. In comparison to manual NDT methods, mechanized NDT is considered to be more reliable for a number of reasons, one of which being that the role of the inspectors and, therewith, the potential for human error, have been reduced. However, human-automation interaction research suggests that in spite of its numerous benefits, automation can lead to new yet unknown risks. One of those risks is inappropriate reliance on automation, which can result in automation misuse and disuse. The aim of this study was to investigate the potential inappropriate use of automation (specificallythe automated aids) in NDT addressing therewith the prevalent belief in the high reliability of automation held by the NDT community. To address this issue, 70 NDT trainees were asked to control the results of an eddy current data evaluation, allegedly provided by an automated aid, i.e. indication detection and sizing software. Seven errors were implemented into the task and it was measured to what extent the participants agreed with the aid. The results revealed signs of both misuse (agreeing with the aid even though it is incorrect) and disuse (disagreeing with the aid even though it is correct) of the aid that can affect the reliability with which inspections are carried out. Whereas misuse could be explained by a lower propensity to take risks and by a decreased verification behavior-possibly due to bias towards automation and complacent behavior-, disuse was assigned to problems in establishing the sizing criterion or to general difficulties in sizing. The implications of these results for the NDT praxis including suggestions for the decrease of automation bias are discussed.

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