摘要: A new fast learning algorithm was presented to solve the large-scale support vector machine (SVM) training problem of aero-engine fault diagnosis.The relative boundary vectors (RBVs) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly,the training time was shortened to 1/20 compared with basic SVM classifier.Meanwhile,owing to the reduction of support vector number,the classification time was also reduced.When sample aliasing existed,the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides,the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5 classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective,reliable and easy to be implemented for engineering application.
摘要: 根据火箭发动机地面试验对测量与控制的要求,设计了一套用于火箭发动机地面试验数据采集与控制的便携式测控系统.测控系统硬件基于NI(National Instruments)公司的USB(universal serial BUS)数据采集与控制设备开发,集测量与控制功能于一体,便携性好,通用性强;软件基于模块化设计思想,采用LabVIEW编程环境开发,人机交互界面友好,通用性强,可扩展性好.该测控系统平台已成功应用于多次发动机地面试验,能满足多种类型的火箭发动机试验对测控的需求.