噪声与振动控制2011，Vol.31Issue(1)：119-122,4.DOI:10.3969/j.issn.1006-1355-2011.01.025
基于Volterra级数和KPCA的旋转机械故障诊断方法研究
Fault Diagnosis Method of Rotating Machinery Based on Volterra Series and KPCA
摘要
Abstract
A new fault diagnosis method based on Volterra series and KPCA is proposed. In this method, firstly the Volterra series of four states, i.e. normal, rotor crack, rotor anb and pedestal looseness, are identified by particle swarm optimization (QPSO) algorithm. Then the Volterra series is used as characteristic vectors to input into the kernel principal component analysis (KPCA) for training and recognition. The experiment result shows that the proposed method is very effective. The higher order Volterra kernels such as the second-order, the third-order kernels can be used for the recognition when the faults can not be distinguished readily with the use of the first-order Volterra kernel only.关键词
振动与波/Volterra级数/量子粒子群(QPSO)/核函数主元分析(KPCA)/故障诊断分类
信息技术与安全科学引用本文复制引用
蒋静,李志农,易小兵..基于Volterra级数和KPCA的旋转机械故障诊断方法研究[J].噪声与振动控制,2011,31(1):119-122,4.基金项目
国家自然科学基金(50775208,51075372),河南省教育厅自然科学基金(2006460005,2008C460003),湖南省机械设备健康维护重点实验室开放基金(200904) （50775208,51075372）
