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

Abstract

Effective and efficient problem solving mechanism is one of the critical processes 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, comprehensive maintenance support to the maintainers in critical events to reduce maintainer 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 model that was consistent with maintainers’ cognitive types was reciprocally affected by fault recovery. On the other hand, a vast amount of maintenance data, which included lots of implicit information, could indicate critical events for the policymaker by statistical analyses in the maintenance domain. These same data were used to reassess which kind of issue should be treated as the first priority. Through interviewing professional maintenance engineers and analyzing documents at maintenance tasks, the development process of a maintenance protocol is applied in the knowledge acquisition implementation. 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 method and expert system provided a systematic procedure for maintenance engineers and managers to retrieve fault cases quickly and accurately, and to effectively accumulate their expertise for logistic adaptation. The FRMM conceptual model could serve as a guide for other similar logistic systems to prevent maintainer errors.