Title :
Anomaly detector fusion processing for advanced military aircraft
Author :
Brotherton, Tom ; Mackey, Ryan
Author_Institution :
Intelligent Autom. Corp., San Diego, CA, USA
Abstract :
Automated Prognostics and Health Management (PHM) is a requirement for advanced military aircraft. PHM is the key to achieving true condition-based maintenance. PHM processing strategies include modules for the processing of known nominal and fault conditions. However in real operations there will also occur faults and other off-nominal operations that were never anticipated nor ever encountered before. We call these events anomalies. Missing the presence of an anomaly could potentially be catastrophic with the loss of the pilot and aircraft. Several different anomaly detectors (ADs) have been developed for advanced military aircraft to solve this problem. Fusion of these ADs can significantly reduce false alarms while at the same time substantially improving detection performance. Fusion is a way of approaching the goal of perfect detection with zero false alarms. We have developed a neural net approach for performing AD fusion. Presented is a description of that technique and the application to military aircraft subsystem data
Keywords :
aerospace expert systems; aircraft computers; aircraft maintenance; aircraft power systems; computerised monitoring; fault diagnosis; learning (artificial intelligence); military aircraft; military avionics; military computing; radial basis function networks; sensor fusion; vector quantisation; LMS weighting; RBF network; advanced military aircraft; aircraft subsystem data; anomaly detector fusion processing; automated prognostics and health management; auxiliary power unit; condition-based maintenance; cross-signal anomaly detector; false alarms reduction; fault conditions; flight control hydraulic system; linear VQ; neural net approach; off-nominal operations; Aircraft propulsion; Automation; Detectors; Hidden Markov models; Least squares approximation; Military aircraft; Multi-layer neural network; Multilayer perceptrons; Neural networks; Prognostics and health management;
Conference_Titel :
Aerospace Conference, 2001, IEEE Proceedings.
Conference_Location :
Big Sky, MT
Print_ISBN :
0-7803-6599-2
DOI :
10.1109/AERO.2001.931330
