Knowledge architecture and framework design for preventing human error in maintenance tasks

Effective and efficient problem solving mechanism is one of the critical pr ocesses that ensure a good service quality in the maintenance environment. Maintenance errors can be easily induced by the time stress due to frequent task varieties and logistic decision uncertainties. In the sense, comprehe nsive maintenance support to the maintainers in critical events to reduce m aintainer errors was strongly suggested. A practical framework is proposed for analyzing cognitive types and enhancing fault recovery ability through knowledge-based system. It has shown that a suggested hybrid cognitive mode l that was consistent with maintainers' cognitive types was reciprocally af fected by fault recovery. On the other hand, a vast amount of maintenance d ata, which included lots of implicit information, could indicate critical e vents for the policymaker by statistical analyses in the maintenance domain . These same data were used to reassess which kind of issue should be treat ed as the first priority. Through interviewing professional maintenance eng ineers and analyzing documents at maintenance tasks, the development proces s of a maintenance protocol is applied in the knowledge acquisition impleme ntation. Based on human experts' domain-specific knowledge sharing and well -preserved documents utilizing, a fault recovery management mechanism (FRMM ) was developed. Such integration of reliability-centered maintenance metho d and expert system provided a systematic procedure for maintenance enginee rs and managers to retrieve fault cases quickly and accurately, and to effe ctively accumulate their expertise for logistic adaptation. The FRMM concep tual model could serve as a guide for other similar logistic systems to pre vent maintainer errors. (C) 2000 Elsevier Science Ltd. All rights reserved.