Improvement of Aircraft Availability and Optimization of Component Costs by Pre-Emptive Removal of Targeted Components 2019-01-1341

Availability of large repairable systems, like aircraft, are critical for commercial operators to generate revenue, and for military organizations to achieve their mission readiness objectives. Of the relatively few studies that deal with improving availability, most have focused on increasing reliability, and not on the biggest driver of low availability - Unscheduled Maintenance Events (UMEs). The cost of maintenance has long been a target of cost-cutting measures, and one common strategy focuses on extracting as much service life as possible out of various non-critical system components by letting those components “run to failure” (as defined in SAE JA1012). However, one of the biggest drawbacks of the “run to failure” approach is that it comes at the cost of lower asset availability because the failure of one of those components will nearly always lead to a UME, typically just when the operator wants to use, or is currently using, that asset. To combat the impact of UMEs, many OEMs, operators, and component manufacturers are looking to prognostics to get advanced notice of impending failures, so monitored components can be replaced before they completely fail. But, for technological and/or economic reasons, prognostics are not a viable option for the vast majority of components. Furthermore, the idea that running components to failure will reduce costs is fundamentally flawed because it fails to account for the extra operational costs incurred from those UMEs. As an alternative to running components to failure or relying only on prognostics, asset operators and maintainers need other strategies to minimize the operational impact of UMEs for components without prognostics that also balances component utilization and the operational costs associated with UMEs against overall asset availability. This paper presents a methodology for evaluating the trade-offs between these factors and shows how this approach can potentially reduce overall asset life-cycle costs.