Blind Deconvolution De-noising for Helicopter Vibration Data

Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition based maintenance (CBM) requires that the health of critical components/systems be monitored and diagnostics/prognostic strategies be developed to detect and identify incipient failures and predict the failing component´s remaining useful life (RUL). Typically, vibration and other key indicators on-board on aircraft are severely corrupted by noise thus curtailing our ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution de-noising scheme that employs vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed scheme.