Title :
Detection and classification of faults from helicopter vibration data using recently developed signal processing and neural network techniques
Author :
Solorzano, Manuel R. ; Ishii, Dexter K. ; Nickolaisen, Niel R. ; Huang, William Y.
Author_Institution :
US Naval Ocean Syst. Center, San Diego, CA, USA
Abstract :
The feasibility of using nontraditional methods of helicopter transmission fault classification is studied. Various signal processing and classifier techniques are investigated. All algorithms successfully classified the fault samples from a tail rotor transmission dataset. A hardware neural network system was designed and implemented. The relatively low resolution of the neural network circuitry required extensive preprocessing and scaling of the large dynamic range input signal. Results from the hardware system were similar to those achieved in simulation. It is pointed out that a true test of the techniques presented may require a dataset that is statistically richer
Keywords :
aerospace testing; automatic test equipment; fault location; helicopters; neural nets; pattern recognition; signal processing; vibration measurement; classifier techniques; dataset; fault detection; hardware system; helicopter transmission fault classification; helicopter vibration data; large dynamic range input; low resolution; mechanical faults; neural network techniques; nontraditional methods; preprocessing; scaling; signal processing; simulation; tail rotor transmission dataset; Circuit faults; Data preprocessing; Dynamic range; Fault detection; Helicopters; Neural network hardware; Neural networks; Signal processing algorithms; Signal resolution; Tail;
Conference_Titel :
Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-2470-1
DOI :
10.1109/ACSSC.1991.186625