An Innovative Structural Fatigue Monitoring Solution for General Aviation Aircraft

Authors

  • Christopher Keryk
  • Roberto Sabatini RMIT University, School of Engineering Intelligent Transport Systems Research Group
  • Kyriakos Kourousis University of Limerick
  • Alessandro Gardi RMIT University, School of Engineering Intelligent Transport Systems Research Group
  • Jose M. Silva RMIT University, School of Engineering Intelligent Transport Systems Research Group

Keywords:

Aviation safety, Fatigue, General aviation, Cycle counting, Dynamic load, Manoeuvre identification, Structural integrity, Structural healt, Vehicle health.

Abstract

This article proposes a novel and effective solution for estimating fatigue life of General Aviation (GA) airframes using flight data produced by digital avionics systems, which are being installed or retrofi tted into a growing number of GA aircraft. In the proposed implementation, a fl ight dynamics model is adopted to process the recorded fl ight data and to determine the dynamic loadings experienced by the aircraft. The equivalent loading cycles at fatigue-critical points of the primary structure are counted by means of statistical methods. For validation purposes, the developed approach is applied to fl ight data recorded by a fl eet of Cessna 172S aircraft fi tted with a Garmin G1000 integrated navigation and guidance system. Based on the initial experimental results and the developed uncertainty analysis, the proposed approach provides acceptable estimates of the residual fatigue life of the aircraft, thereby allowing a cost-effective and streamlined structural integrity monitoring solution. Future developments will address the possible adoption of the proposed method for unmanned aircraft structural health monitoring, also considering the accuracy enhancements achievable with advanced navigation and guidance architectures based on Global Navigation Satellite Systems (GNSS), Vision-Based Navigation (VBN) Sensors, Inertial Measurement Units (IMU) and Aircraft Dynamics Model (ADM)augmentation.

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Published

2018-02-26

Issue

Section

Original Papers