Supporting usage-driven maintenance decision making for military assets

Abstract

Delivering air power is the main goal of an Air Force. Military aircraft used to deliver this air power are required to be maintained in accordance with a so-called Aircraft Maintenance Program (AMP). Utilization of military aircraft is assumed to be constant when constructing an AMP. In reality, usage can drastically change over both short- and long-term periods as a result of political choices, as well as sudden deployments and changing mission types. These changes in usage will have its effect on system condition and by extension on required maintenance. In order to guarantee optimal efficiency and effectiveness of the AMP, these changes in usage should be taken into account while reviewing the AMP. Health and Usage Monitoring Systems (HUMS) can be used to support in analysing usage on a detailed level. A tool which is able to incorporate both historical and (expected) future usage will be of great help to a maintenance engineer responsible for reviewing the AMP on a yearly basis.
In this research, a tool has been developed able to support in maintenance decisions on interval adjustments and incorporating system usage. Various methods and algorithms are incorporated of which some are novel contributions in their own right. The developed tool uses HUMS data, flight planning data, and design data to provide a possible adjustment on maintenance intervals. The tool takes both historical usage and expected future usage into account. The model is able to translate usage profiles (as defined by the operator) into usage on a very detailed level. This translation creates possibilities to determine usage effects on system condition. Translation to this detailed level of usage is achieved by processing HUMS data through rule-based flight regime recognition algorithms. Finally, the model includes a method to carry out a simplified business case to show possible gains resulting from the proposed interval adjustment.
The model is tested by applying case study data originated from the Apache helicopter of the Royal Netherlands Air Force (RNLAF). The used data to train and test the model covers three years of operation: 2014-2016. Concerning accrued fatigue damage, case study results show an average severity factor of 0.37. This factor is based on the accrued fatigue damage resulting from usage conform the design spectra as defined by the Original Equipment Manufacturer (OEM). The maintenance intervals, currently used by the RNLAF and defined in the AMP, are based on these design spectra. Validation of the model is carried out by making use of a dedicated validation dataset (covering the year 2017). Both historical and expected (extreme severe) future usage are incorporated to set up business cases for three Fatigue Life Limited (FLL) Critical Safety Items (CSIs). These business cases revealed potential gains in component and maintenance cost when adapting the proposed interval adjustment. For these three components, the model proposed interval escalations of 42%, 44%, and 94% where both historical and expected future usage is taken into account.