Health monitoring of vibration signatures in rotorcraft wings

We propose to design and evaluate an on-board intelligent health assessment tool for rotorcraft machines, which is capable of detecting, identifying, and accommodating expected system degradations and unanticipated catastrophic failures in rotorcraft machines under an adverse operating environment. A fuzzy-based neural network paradigm with an online learning algorithm is developed to perform expert advising for the ground-based maintenance crew. A hierarchical fault diagnosis architecture is advocated to fulfil the time-critical and on-board needs in different levels of structural integrity over a global operating envelope. The research objective is to experimentally demonstrate the feasibility and flexibility of the proposed health monitoring procedure through numerical simulations of bearing faults in USAF MH-53J PAVE LOW helicopter transmissions. The proposed fault detection, identification and accommodation architecture is applicable to various generic rotorcraft machines