The Virtual Human Reliability Analyst

Author(s):  
Martin Rasmussen ◽  
Ronald Boring ◽  
Thomas Ulrich ◽  
Sarah Ewing
2009 ◽  
Author(s):  
Ronald Laurids Boring ◽  
Johanna Oxstrand ◽  
Michael Hildebrandt

2019 ◽  
Vol 7 (2B) ◽  
Author(s):  
Vanderley Vasconcelos ◽  
Wellington Antonio Soares ◽  
Raissa Oliveira Marques ◽  
Silvério Ferreira Silva Jr ◽  
Amanda Laureano Raso

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. This inspection is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI is reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components, such as FMEA (Failure Modes and Effects Analysis) and THERP (Technique for Human Error Rate Prediction). An example by using qualitative and quantitative assessesments with these two techniques to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues, is presented.


2010 ◽  
Vol 30 (11) ◽  
pp. 3084-3086
Author(s):  
Qian LI ◽  
Xiao-min JI ◽  
Ming-liang WANG

Author(s):  
Ronald Boring ◽  
Thomas Ulrich ◽  
Torrey Mortenson ◽  
David German

This paper provides background on the process to enhance human reliability analysis (HRA) for long-duration space applications. While short-duration missions largely mirror ground activities and fit well with existing HRA methods, new missions to the Moon or Mars entail a significantly longer duration of time in space for astronauts. This extended period in space presents opportunities to affect astronaut performance that require consideration of new performance shaping factors (PSFs). In the present paper, we conducted a meta-analysis on fatigue and developed a new PSF to account for chronic sleep deprivation associated with long-duration space missions. Fatigue provides a starting point for additional PSFs needed for space HRA.


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