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

Abstract: The algorithms of the failure detection and diagnosis of liquld propellant rocketengines play a key role in the heaIth monitoring techniques. According to the stage of the engineoperation, deferent detection and diagnosis algorithms are developed. A model based detectionalgorithm is constructed via using the Extended Karlman Filtering methed’ Reduced order failureresponse medels are used to develop medel based failure diagnosis algorothms. To improve the realtime detection performance, neural network based identification model is constructed for the failuredetection of the starting process of the engine, and time sequence medel is developed for detection ofthe failure during the engine main-stage process. The efficiency of the detection algorithms is verifiedthrough test data based computation.

Key words: Liquid propellant rocket engine;Engine fault;Fault diagnosis;Fault detection;Artificial neural network;Real time algorithm