Reducing Military Helicopter Maintenance Through Prognostics

Abstract

For aircraft engineers and maintainers, the risk of faults and failures within the mechanical assembly of a helicopter has always been a major concern. It is the nature of a rotary-wing aircraft to have major components such as gearboxes and rotors in single load-path configurations. This means that the consequence of mechanical failure is often catastrophic and frequently fatal. In recognition of these facts, the scheduled maintenance practices for helicopters have always been set to contain large safety margins; minimising the risk of failures at the expense of over-maintenance. Since the late 1990s, the UK Ministry of Defence (MOD) has embarked on an ambitious programme to fit Health and Usage Monitoring Systems (HUMS) to its helicopters, with the Chinook Mk2/2a programme being in the vanguard; the only fleets fully implemented. These sophisticated monitoring tools provide an accurate measurement of the immediate state of mechanical health for individual helicopters and a real-time measurement of the usage those aircraft experience. From these two sources of information a projection (prognosis) of the trend of mechanical health may be devised and continuously monitored and the need for most forms of mechanical maintenance can be predicted. This should allow operators to reduce both of the maintenance cost burdens it bears. It should enable the broad safety margins set in scheduled maintenance intervals to be reduced and allow maintainers to predict unscheduled spares requirement in advance of the actual maintenance event. Such advance notice of maintenance requirement would enable lean efficiencies in the supply chain to be achieved. This paper introduces the concepts behind HUMS, prognostics, maintenance optimisation and lean thinking. It then presents a mathematical model for the maintenance and lean supply-chain efficiencies that could be achieved through prognostic analysis of real-time mechanical health information from Chinook HUMS. Simulation results are introduced and discussed demonstrating the potential reduction in D states (aircraft grounded for lack of spares), maintenance queues and deployed component stockholding.