Model-based Remaining-Useful-Life prognostics for aircraft Cooling Units

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Prognostics for the Remaining-Useful-Life (RUL) of aircraft components are crucial to support efficient aircraft maintenance planning and, in particular, to limit unscheduled maintenance due to unexpected component failures. As such, predictive methods for the RUL of aircraft components are increasingly a priority for aircraft Maintenance, Repair and Operations (MROs). In this paper we develop model-based RUL prognostics for aircraft Cooling Units using operational data recorded during the flights of several wide-body aircraft. A Cooling Unit is a vapor cycle refrigeration unit consisting of a condenser, a flash tank, an evaporator and a compressor. After some time of usage, the filter of these Cooling Units is clogged with burned oil, moist and sludge from the compressor. This accelerates the wear of the components. Long time utilization of these components in these conditions leads to a failure. To model the degradation of the Cooling Units, we use an exponential functional form for the degradation. Together with sequential Monte Carlo methods, we estimate the probability distribution of the RUL of these components. The exponential functional form of the degradation is based on the fact that the cumulative damage in the components has an effect on the degradation rate. It has been shown that an exponential model is a good approximation for non-linear degradation processes like corrosion, bearing degradation, or deterioration of LED lighting. In fact, the Cooling Units can also be seen as subject to corrosion and accelerated wear. We evaluate our RUL prognostics for various prediction horizons, i.e., at 30, 20 and 10 flight cycles before failure. The results show that our proposed methodology is able to estimate the RUL of the Cooling Units well, and that the uncertainty associated with the prognostics decreases as the prediction horizon decreases, i.e., as the components approach failure. The choice of the prediction horizon is relevant from the point of view of MROs, which re-evaluate periodically their aircraft maintenance schedules. In practice, regular maintenance checks are scheduled every two weeks. Having accurate RUL prognostics over such time horizons enables the maintenance planners to better plan tasks, limiting unscheduled failures. In addition, the fact that we estimate the uncertainty associated with the RUL prognostics enables the maintenance planners to prioritize the maintenance of the components. Overall, our results provide support for maintenance planners to make informed and efficient maintenance schedules.