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- 4-Eye Principle (1)
- Automation Bias (1)
- Bayesian Approach (1)
- Bayesian statistics (1)
- Eye Tracking (1)
- Failure Modes and Effects Analysis (FMEA) (1)
- Human Factor (1)
- Human Factors (1)
- In-Service Inspection (ISI) (1)
- Modular Model (1)
This paper gives an introduction to the field of human factors with the focus on their influence on the reliability of NDT in the nuclear energy production (in-service inspections) and final storage of highly radioactive nuclear waste. A set of methodological tools has been developed in the scope of three projects, namely: 1) a theoretical model describing potential human factors influencing manual ultrasonic inspection performance during inservice inspections in nuclear power plants; 2) a method for identifying potential human errors during acquisition and evaluation of data gathered with mechanized ultrasonic, radiographic and eddy-current systems, as well as visual testing with a remote camera (Failure Modes and Effects Analysis, FMEA); and 3) use of eye tracking methodology to optimize existing procedures and practices. The experimental results have shown that time pressure, mental workload and experience influence the quality of the inspection performance. Noticeable were influences from the organization of the working schedule, communication, procedures, supervision and demonstration task. Implementing human redundancy in critical tasks, such as defect identification, as well as using an automated aid (software) to help operators in decision making about the existence and size of defects, could lead to other kinds of problems, namely social loafing (excerpting less effort when working on tasks collectively as compared to working alone) and automation bias (uncritical reliance on the proper function of an automated system without recognizing its limitations and the possibilities of automation failure) that might affect the reliability of NDT in an undesired manner.
The Probability of Detection (POD) is used to evaluate the detectability of non-destructive testing (NDT) systems. The POD is highly dependent on the amount of available data. The Bayesian approach provides a solution to compute POD-curves in case of a small amount of real defects without losing the necessary information. The result contains the needed information for the computation of POD-curves for real defects with an acceptable amount of information, even for sparse amount of data. In this paper is shown limitations of the Bayesian approach and how it can be applied to NDT. The Bayesian approach is applied in this case to the evaluation of radiographic testing. Bayesian approach is applied to determine POD-curves for the inspection techniques of nuclear fuel disposal canisters. The reason for using Bayesian approach is the high safety demands and also the low amount of real defects due to the high quality of the reliable production techniques.
New methodologies for evaluating the reliability of NDE systems are discussed in accordance with the specific
requirements of industrial application. After a review of the substantive issues from the previous decades, the go
forward guidance is concluded.
For high safety demands a quantitative probability of detection (POD) created from hit miss
experiments or signal response analysis and ROC (Receiver Operating Characteristics) are typically created. The
modular model distinguishes between the influence of pure physics and technique, industrial application factors
and the human factor and helps to learn what factors are covered by modelling, 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 use it for optimisation 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, the human factor is of high importance. A systematic psychological
approach helps to find out where the bottlenecks are and shows possibilities for improvement.