Quickest change detection, statistical learning and nonlinear filtering of jet engine data

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Modern jet engines are some of the most expensive components on an aircraft and are engineered to be extremely reliable at great cost (up to $25 million per engine). Yet they can still experience unexpected catastrophic failure, resulting in tragedy and loss of life. In order to monitor engine health and performance, each engine is equipped with about 100 sensors that measure engine performance parameters, from pressure and temperature of the engine gas path to vibration of the rotating components. The goals of the proposed collaborative research are to develop novel mathematical and statistical methods that exploit new sensing capabilities on engines for (i) constructing new anomaly-detection schemes and quickest-change detection algorithms; and (ii) discovering instability limits, thus preventing damage to engine components and lengthening their life-span. The research proposed here uses both model-based and data-driven theories to develop efficient numerical algorithms for quickest change detection, statistical learning and nonlinear filtering targeted at our specific problem. Theme 1, the proposed quickest-change detection, will be a vital procedure for engine performance monitoring. Once a change in an engine state has occurred, that change must be detected as soon as possible, while minimizing false detections. Theme 2 of the proposal focuses on the data-driven methods appropriate for data generated by jet engines. When no explicit dynamical model is available, system knowledge boils down to real-time measurements, possibly complemented by process history. Advances made in collaboration with the industry partner, TECSIS Corporation, will have a lasting impact on the Prognosis and Health Management (PHM) techniques for detecting abrupt changes or anomalies in gas turbine engines. The proposed data-centric methods will benefit the aerospace industry at large as well as open new research perspectives and domains that rely heavily on measurements for system monitoring and control.