title: Analog neural network-based helicopter gearbox health monitoring system.
abstract: 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/1) 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/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.
authors.affiliation: Charles Stark Draper Laboratory, Cambridge, Massachusetts 02139, USA.
authors.forename: P T
authors.initials: PT
authors.lastname: Monsen
authors.forename: M
authors.initials: M
authors.lastname: Dzwonczyk
authors.forename: E S
authors.initials: ES
authors.lastname: Manolakos
mesh_terms: D000401:Aircraft; D004867:Equipment Design; D008962:Models, Theoretical; D016571:Neural Networks, Computer