We propose to design and to 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 on-line learning algorithm is developed to perform expert advising for the ground-based maintenance crew. A hierarchical fault diagnosis architecture is advocated to fulfill 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. The proposed system will greatly reduce the operational and developmental costs and serve as an essential component in an autonomous control system.
[1]
J. C. Deckert,et al.
Evaluation of the F-8 DFBW analytic redundancy sensor FDI algorithm using telemetry data. [Failure Detection and Identification for Digital Fly-By-Wire control]
,
1977
.
[2]
Richard Vernon Beard,et al.
Failure accomodation in linear systems through self-reorganization.
,
1971
.
[3]
H.E. Rauch.
Intelligent fault diagnosis and control reconfiguration
,
1994,
IEEE Control Systems.
[4]
Gary G. Yen.
Reconfigurable learning control in large space structures
,
1994,
IEEE Trans. Control. Syst. Technol..
[5]
Kourosh Danai,et al.
Fault Detection of Helicopter Gearboxes Using the Multi-Valued Influence Matrix Method
,
1993
.