关键词: 液体推进剂火箭发动机;故障检测;故障诊断;模式识别;数值仿真

Abstract: Presents a GA-HCM hybrid clustering algorithm with two leveled "director-student"type of intelligent hierarchy, considering the fatal drawbacks of HCM clustering algorithm due to itssubjectivity to the initial values and learning rate. This GA-HCM hybrid clustering algorithm uses theGenetic Algorithm to select the initial seed solutions f0r the HCM algorithm, which is used to preevaluate the initial seed solutions’ fitness. Meanwhile, this hybrid algorithm has been implemented inthe particular fault detection and diagnosis framework specially designed by us before, in order toevaluate its Performance as a multivariate clustering means for the sliding data vectors window. Thenumerical simulation result shows that this hybrid algorithm is capable of finding a globally optimal ornear-optimal clustering centroids for the sliding data window fast than the only Genetic Algorithm oronly HCM, and completely overcome HCM’S drawbacks listed above. The detection ability for theincipient fauIts ensures it a perspective and practical fault detection algorithm.

Key words: Liquid propellant rocket engine;Fault detection;Fault diagnosis;patternrecognition;Numerical simulation