Optimal cost preventative maintenance scheduling for high reliability aerospace systems

Aerospace systems designed to meet stringent reliability requirements are generally expensive to replace when they fail. Preventative maintenance returning the system to a nearly new condition is often much less expensive than replacement. Selecting a cost effective preventative maintenance interval that balances the costs of replacement due to failure with the costs of periodic preventative maintenance can be challenging. The challenge is exacerbated because highly reliable aerospace systems fail infrequently, providing very little data for classical statistical analysis. The US Coast Guard encountered such a problem with a particular subsystem on their fleet of C130 aircraft. This subsystem was the 1500 Series Flight Deck Cooling Turbine, which if failed in service cost $30,000 to replace, yet cost only $500 to overhaul as preventative maintenance. This cooling turbine had only failed five times, hardly enough data to enable use of any classical statistical method. However, this set of data was sufficient to enable conditional inferential methods to identify the preventative maintenance interval that optimizes maintenance costs at reasonable levels of risk. This report demonstrates the parameterization of cost savings possible as a function of candidate preventative maintenance intervals for the US Coast Guard C130 1500 Series Flight Deck Cooling Turbine. Five failure data and one survivor datum are processed using conditional inferential methods. This parameterization, achieved without using any questionable assumptions, allows selection of an interval that optimizes maintenance costs. This approach may be used to select optimum cost preventative maintenance intervals for any subsystem for any aerospace system.