Aircraft engine health monitoring using density modelling and extreme value statistics

Current practice in the operation and maintenance of an aircraft fleet requires analysis of data obtained from in-service engines in order to identify engine deterioration and provide preventative maintenance. Typically large quantities of engine vibration and performance data are available from various engine-mounted sensors. The analysis of such data requires techniques for modelling these multivariate data allowing fleet specialists to establish profiles of engine behaviour under different operating conditions. Additionally, such techniques can be used to identify precursors of engine events to avoid loss of engine service. This paper describes density modelling techniques for the estimation of the multivariate unconditional data density of performance and vibration parameters acquired from aerospace gas-turbine engines. We set a probabilistic threshold using Extreme Value Theory (EVT). This framework is used to generate reliable, timely alerts concerning abnormal engine operation. Finally, case studies are presented that analyse performance and vibration data obtained from a representative set of civil aircraft engines. Our results show that such techniques can provide reliable identification of abnormal engine events.