The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural computing Architecture (INCA/I) analog neural network platform that was designed and developed at the Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100 % fault detection rate with 0 % false alarm rate by performing a DFT directly on the first layer of INCA/I followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.
Analog neural network-based helicopter gearbox health monitoring system
Analoges neuronales Netzwerk als Lebensüberwachungssystem für Helikoptergetriebe
Journal of the Acoustical Society of America ; 98 , 6 ; 3235-3249
1995
15 Seiten, 15 Bilder, 3 Tabellen, 29 Quellen
Article (Journal)
English