The presentation of knowledge and state-information for system fault diagnosis

System fault diagnosis is often performed by humans using fault diagnosis knowledge such as fault trees and system state information. An experiment was conducted to investigate how various forms of presenting fault trees and system state information affect human performance and preference during fault diagnosis. Three knowledge presentations (a semantic network, a schema-based semantic network, and production rules) and four state-information presentations (direct manipulation, menus, fill-in-forms, commands) were examined in the experiment; 30 subjects participated. The results of the experiment indicated that knowledge presentation by the semantic network yielded better subject performance of fault diagnosis. In addition, direct manipulation was the most favorite form of state information presentation to the subjects. The s-significant interaction between the knowledge presentations and the state information presentations in terms of subject preference was also found.