The development of a hybrid intelligent maintenance optimisation system (HIMOS).

Kobbacy, KAH; Jeon, J

Abstract

This paper reports on the development of a hybrid intelligent maintenance optimisation system (HIMOS) for decision support. It is a follow-up to an earlier paper published in the Journal of the Operational Research Society in 1995. Both papers refer to systems where there are very many components which may break down independently. When a component breaks down, corrective action (CO) is required. The problem is to determine the optimal maintenance policy, essentially the frequency of preventive maintenance (PM) which minimises the sum of down time due to PM and CO.

HIMOS, like its predecessor IMOS, uses an ‘intelligent’ decision support system to carry out an automated analysis of the maintenance history data. Maintenance data are presented to the system and the most suitable mathematical model from a model-base is identified utilising a hybrid knowledge/case based system (KBS/CBR). Thus initially a rule base is applied to select a model, as in the case of IMOS. If no model is matched, the system reverts to its historical case-base to match the current case with a similar case that has been previously modelled. This double reasoning adds to the system's true learning capabilities (intelligence) and increases the rate of success of model selection. A prototype system is written in Visual Basic® for an IBM compatible PC. The study results include optimal PM intervals for a sample of industrial data sets. The results of the validation exercise of HIMOS against expert advice has shown that the system functions satisfactorily.

Citation

Kobbacy, K., & Jeon, J. (2001). The development of a hybrid intelligent maintenance optimisation system (HIMOS). Journal of the Operational Research Society, 52(7), 762-778. https://doi.org/10.1057/palgrave.jors.2601157

• Journal Article Type: Article
• Publication Date: Dec 1, 2001
• Deposit Date: Aug 21, 2007
• Journal: Journal of the Operational Research Society
• Print ISSN: 0160-5682
• Publisher: Palgrave Macmillan
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 7
• Pages: 762-778
• DOI: https://doi.org/10.1057/palgrave.jors.2601157
• Keywords: maintenance; information systems; decision support systems; knowledge-based systems; case-based reasoning; hybrid systems