Detection of helicopter rotor system simulated faults using neural networks

Simulated fault data from a mathematical model of a damaged rotor system is used to develop a neural network-based approach for rotor system damage detection. The mathematical model of the damaged rotor is a comprehensive rotorcraft aeroelastic analysis based on a finite element approach in space and time. Selected helicopter rotor faults are simulated through changes in inertial, damping and stiffness properties of the damaged blade. A feed-forward neural network with back-propagation learning is trained using both 'ideal' and 'noisy' simulated data. Testing of the trained neural network shows that it can detect and identify damage in the rotor system from simulated blade response and vibratory hub loads data. (Author)

[1]  Yubao Chen,et al.  Machinery condition monitoring by inverse filtering and statistical analysis , 1992 .

[2]  Petri Koistinen,et al.  Using additive noise in back-propagation training , 1992, IEEE Trans. Neural Networks.

[3]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[4]  Inderjit Chopra,et al.  Formulation of a rotor-system damage detection methodology , 1995 .

[5]  D. Kammer.  Effects of Noise on Sensor Placement for On-Orbit Modal Identification of Large Space Structures , 1992 .

[6]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[7]  Yubao Chen,et al.  Fuzzy decision system for fault classification under high levels of uncertainty , 1995 .

[8]  Masahiro Abe,et al.  Incipient fault diagnosis of chemical processes via artificial neural networks , 1989 .

[9]  Gary G. Yen,et al.  NEURAL NETWORK APPROACH FOR THE DEMAGE DETECTION OF STRUCTURES , 1993 .

[10]  Inderjit Chopra,et al.  Formulation of a Helicopter Rotor System Damage Detection Methodology , 1996 .

[11]  Prabhat Hajela,et al.  Recent developments in damage detection based on system identification methods , 1990 .

[12]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[13]  Bernhard R. Kämmerer,et al.  Incorporating Uncertainty in Neural Networks , 1992, Int. J. Pattern Recognit. Artif. Intell..

[14]  Robert E. Uhrig,et al.  Using modular neural networks to monitor accident conditions in nuclear power plants , 1992, Defense, Security, and Sensing.

[15]  H Azzam,et al.  The use of Math-Dynamic Models to Aid the Development of Integrated Health and Usage Monitoring Systems , 1992 .

[16]  M. Scully,et al.  Computation of helicopter rotor wake geometry and its influence on rotor harmonic airloads , 1975 .

[17]  J. Leishman.  Modeling of Subsonic Unsteady Aerodynamics for Rotary Wing Applications , 1990 .