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|Title:||Human Pattern Recognition and Information Seeking in Simulated Fault Diagnosis Tasks|
|Author(s):||Hunt, Ruston McClellan|
|Department / Program:||Mechanical Engineering|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|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.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981.
|Date Available in IDEALS:||2014-12-13|
This item appears in the following Collection(s)
Dissertations and Theses - Mechanical Science and Engineering
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois