Abstract
The classification of nuclear power plant procedures at the sub-task level can be accomplished via text mining. This method can inform dynamic human reliability calculations without manual coding. Several approaches to text classification are considered with results provided. When a discrete discriminant analysis is applied to the text, this results in clear identification procedure primitive greater than 88% of the time. Other analysis methods considered are Euclidian difference, principal component analysis, and single value decomposition. The text mining approach automatically decomposes procedure steps as Procedure Level Primitives, which are mapped to task level primitives in the Goals, Operation, Methods, and Section Rules (GOMS) human reliability analysis (HRA) method. The GOMS-HRA method is used as the basis for estimating operator timing and error probability. This approach also provides a tool that may be incorporated in dynamic HRA methods such as the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework.
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Acknowledgments
Every effort has been made to ensure the accuracy of the findings and conclusions in this paper, and any errors reside solely with the authors. This work of authorship was prepared as an account of work sponsored by Idaho National Laboratory, an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Idaho National Laboratory is a multi-program laboratory operated by Battelle Energy Alliance LLC for the United States Department of Energy under Contract DE-AC07-05ID14517.
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Ewing, S.M., Boring, R.L., Rasmussen, M., Ulrich, T. (2018). Text Mining for Procedure-Level Primitives in Human Reliability Analysis. In: Boring, R. (eds) Advances in Human Error, Reliability, Resilience, and Performance. AHFE 2017. Advances in Intelligent Systems and Computing, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-319-60645-3_24
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DOI: https://doi.org/10.1007/978-3-319-60645-3_24
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