A neural network based approach for rotor system load monitoring has been developed and evaluated in a flight test of an instrumented SH-60 aircraft. Vibratory loads are predicted using aircraft state parameters which are typically measured and stored on a flight data recorder. The network input parameters are chosen such that the complexity of transmitting signals from the rotating system into the fixed helicopter system is avoided. The load prediction algorithms are developed using an extensive SH-60 flight data set which includes 56 flights and a wide variety of SH-60 usage spectrum maneuvers. The real-time load monitoring capability was demonstrated in a flight test evaluation where the algorithms were embedded in an on-board processor and executed in real time. Network predictions are compared with rotor system strain gauge measurements from the flight test aircraft. Assessment of the load predictions shows good results and indicates that the neural network algorithms, developed using flight data from one aircraft, can be used to predict vibratory loads on a second aircraft of the same class. The flight test results also demonstrate the potential to use the algorithms for real-time pilot cueing to minimize operation in fatigue damaging flight conditions.
Development and flight test evaluation of a rotor system load monitoring technology
Entwicklung und Validierung eines Systems zur Überwachung der Rotorbelastung
Journal of the American Helicopter Society ; 45 , 1 ; 19-27
2000
9 Seiten, 24 Bilder, 4 Tabellen, 9 Quellen
Aufsatz (Zeitschrift)
Englisch
Dehnungsmessung , Flug , Hubschrauber , Messdatenerfassung , neuronales Feedforward-Netz , Pleuelstange , Rotor (Drehflügel) , Schwingbeanspruchung , Schwingungsdämpfer , Schwingungsspektrum , Vorhersage , Wechsellast (mechanisch) , Rechenprogramm , Rechengenauigkeit , Spannungsberechnung , Lernalgorithmus , Validierung , Belastungsprüfung (Mechanik)
Development and Flight Test Evaluation of a Rotor System Load Monitoring Technology
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