Human Pattern Recognition and Information Seeking in Simulated Fault Diagnosis Tasks
Hunt, Ruston McClellan
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/67038
Description
Title
Human Pattern Recognition and Information Seeking in Simulated Fault Diagnosis Tasks
Author(s)
Hunt, Ruston McClellan
Issue Date
1981
Department of Study
Mechanical Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Industrial
Computer Science
Language
eng
Abstract
An experiment was conducted in which thirty-four subjects solved a total of 5440 simulated fault diagnosis problems over a period of ten weeks. Two simulations were used; a context-free simulation known as TASK and a context-specific simulation known as FAULT. Results showed that context-free computer aiding helped to reduce the number of errors committed on the context-specific simulation.
A fuzzy rule-based model was developed to match human problem solving behavior. The model was able to match 50% of the subjects' actions exactly and it used the same rules approximately 70% of the time. Problem solving rules were selected by the model according to measures of recall, applicability, usefulness, and simplicity. Rules were further discriminated by their use of symptomatic information for pattern recognition (S-Rules) or topographic information for information seeking (T-Rules). The overall results are discussed with regards to the implications for fault diagnosis training.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
