Determining Interdependencies and Causation of Vibration in Aero Engines Using Multiscale Cross-Correlation Analysis

Abstract

Aircraft engines are one of the most heavily instrumented parts of an aircraft, and the data from various types of instrumentation across these engines are continuously monitored both offline and online for potential anomalies. Different measurements (temperatures, vibration, etc.) are influenced by various flight parameters (e.g. throttle position) and environmental conditions (outside temperature, pressure, humidity, etc.). Identification of the mutual interactions and causation underpins the understanding of emerging structures in such a complex system in which key parameters might be nonlinearly dependent. A simple cross-correlation analysis among the different sensors would fall short in an effort to paint a complete picture as the system involved is multifractal and evolves in multiple time scales with strong non-stationary signals. In the present case of aero-engine responses, dynamics among the different parts of the engine are particularly complex and understanding the cross-correlation among different parameters would enable the development of a data-driven model for quantities of interest.

In the current work, firstly, to identify the mutual interaction of different sensor parameters to vibration, multi-scale de-trended partial cross-correlation analysis (MS-DPCCA) coefficient method is applied to various parameters on the aero-engine gas path (e.g. temperature, rotor speed, etc). Secondly, a new approach to model building is carried out using the state of the art input-output Dynamic Mode Decomposition (ioDMD) combined with a multi-resolution approach to estimate the state coefficients. The model is validated using a completely different engine test data.