A knowledge-based prototype for optimization of preventive maintenance scheduling

A methodology has been developed, and a prototype tool, the Maintenance Advisor, has been designed and implemented based on this methodology which would assist the scheduling and decision-making for performance of preventive maintenance activities in a plant, based on probabilistic judgedment and probabilistic inference rules. Using data on failure rates, repair times, repair costs and indirect economic costs (e.g. power replacement and accident risk), and within the imposed deterministic constrainst, the program develops an optimum (minimum expected cost) maintenance schedule for the various pieces of equipment described by the model.

The Maintenance Advisor is a frame-based object-oriented tool, programed in KEE and Lisp. Equipment and other objects are represented as complex units, containing a complete set of characteristics, data and functional capabilities. Functional relations between the units are described in terms of two relations: TYPE-OF and PART-OF. The hierarchies formed by these relations serve as the basis for probabilistic and other inferences.