Data-Driven Modeling of Vibrations in Turbofan Engines Under Different Operating Conditions

Vibration monitoring in aircraft engines is traditionally performed using order tracking methodology. Currently, there are no representative physics-based models for tracked order vibration in aircraft engines. This gap in research is primarily attributed to the limited understanding of mutual interactions and the nonlinear nature of engine vibrations. The objective of the current study is twofold: 1) to present a preliminary investigation of tracked order vibrations in aircraft engines and to statistically analyze them in the context of their operating environment and 2) to leverage emerging data-driven modeling techniques to develop accurate models for the tracked order vibration in a turbofan engine valid over a wide range of operating conditions. Toward the same, we employ multiscale detrended partial cross-correlation analysis to evaluate the interdependencies and cross-correlation structure between various thermomechanical variables and tracked order vibration across multiple time scales. We leverage the understanding of complex correlation structures and subsequently use them to model tracked order vibrations. We develop datadriven models using state-of-the-art time-delay embedded dynamic mode decomposition and compare its performance with Lasso regression.