关键词: 液体推进剂火箭发动机;发动机故障;故障诊断;人工神经元网络;实时显示

Abstract: A system’s architecture for condition monitoring of propulsion system using BP ART hybrid neural networks was presented The topology of this hybrid architecture was:the first processing unit consisted of BP(Back Propagation)neural networks,one BP network per subassembly,the second unit was made up of ART(Adaptive Resonance Theory)neural network which had an autoassociative architecture,only one network for the whole propulsion system Each output of ART network represented a“health state”of propulsion system The hybrid architecture was made full uses of advantages of every neural network An application example of this system wasA system’s architecture for condition monitoring of propulsion system using BP ART hybrid neural networks was presented The topology of this hybrid architecture was:the first processing unit consisted of BP(Back Propagation)neural networks,one BP network per subassembly,the second unit was made up of ART(Adaptive Resonance Theory)neural network which had an autoassociative architecture,only one network for the whole propulsion system Each output of ART network represented a“health state”of propulsion system The hybrid architecture was made full uses of advantages of every neural network An application example of this system was illustrated,which demonstrated that this hybrid neural networks’ system could be implemented effectively in developing an advanced real time system for condition monitoring and fault diagnosis of propulsion system

Key words: Liquid propellant rocket engine;Engine failure;Fault diagnosis;Artificial neural network;Real time display