Predictive component availability optimization with degradation correlated maintainability. (November 2013)

• Record Type: Journal Article
• Authors: You, Ming-Yi
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 227:Number 4(2013)
• Page Start: 243
• Page End: 253
• Publication Date: 2013-11
• Subjects: Availability -- availability optimization -- reliability -- maintainability -- predictive maintenance -- maintenance scheduling; Mechanical engineering -- Periodicals; Production engineering -- Periodicals; Manufacturing processes -- Periodicals; 621.05
• DOI: 10.1177/0954408912456661
• Languages: English
• ISSNs: 0954-4089

Abstract

Driven by practical maintenance experience and the desire for higher component availability, this article presents a predictive maintenance policy for maximizing the availability of a batch of components (or single-component machines) in one repair cycle based on their real-time degradation signals, where the maintainability of preventive maintenance acts is explicitly correlated with the degradation levels accumulated before the preventive maintenance acts. The degradation correlated maintainability is modelled by a recently proposed proportional repair model. Within the proposed predictive maintenance policy, each component's reliability is calculated and updated using its real-time degradation signals. The results from a numerical experiment based on a typical degradation model show that, for a batch of components whose maintainability of preventive maintenance acts is indeed correlated with the damage/degradation level accumulated before the preventive maintenance acts, it will lead to suboptimal preventive maintenance schedules if the effect of damage/degradation level on maintainability is ignored. In addition, the experimental results provide evidence of the superiority of the predictive maintenance policy over the corresponding preventive maintenance policy in terms of achieving higher average availability, by considering the real-time degradation signals of each individual component.