A Data-Driven Signal Processing Framework for Enhanced Freeplay Diagnostics in NextGen Structural Health Monitoring Systems
In this paper, a practical signal processing framework is presented, based upon established nonlinear system identification methods, to rapidly diagnose structural freeplay anomalies in aircraft systems. The framework exploits rich bilinear signatures encoded in time-domain sensory outputs via Higher-Order Spectra (HOS) and Empirical Mode Decomposition (EMD). A case-study of an aircraft all-movable horizontal tail with actuator freeplay is presented to validate the applicability of the identification framework. Using surrogate flight-test data with selected test points, it is shown that the freeplay location and magnitude information can be extracted with a high level of robustness. This is validated by making consistent predictions of freeplay anomalies over a period of three years and several aircraft maintenance cycles. The paper is intended to communicate the fundamental principles and significance of the data-driven framework, highlighting that the proposed nonlinear identification tools can address the requirements of contemporary SHM. However, practical implementation requires ongoing research to address data acquisition limitations, which is the scope of future work.
