A METHODOLOGY FOR COLLECTION AND ANALYSIS OF HUMAN ERROR DATA-BASED ON A COGNITIVE MODEL - IDA

This paper presents a model-based human error taxonomy and data collec tion. The underlying model, IDA (described in two companion papers), i s a cognitive model of behavior developed for analysis of the actions of nuclear power plant operating crew during abnormal situations. The taxonomy is established with reference to three external reference poi nts (i.e. plant status, procedures, and crew) and four reference point s internal to the model (i.e. information collected, diagnosis, decisi on, action). The taxonomy helps the analyst: (1) recognize errors as s uch; (2) categorize the error in terms of generic characteristics such as 'error in selection of problem solving strategies' and (3) identif y the root causes of the error. The data collection methodology is sum marized in post event operator interview and analysis summary forms. T he root cause analysis methodology is illustrated using a subset of an actual event. Statistics, which extract generic characteristics of er ror prone behaviors and error prone situations are presented. Finally, applications of the human error data collection are reviewed. A prima ry benefit of this methodology is to define better symptom-based and o ther auxiliary procedures with associated training to minimize or prec lude certain human errors. It also helps in design of control rooms, a nd in assessment of human error probabilities in the probabilistic ris k assessment framework. (C) 1997 Elsevier Science S.A.