［1］梅宏斌.滚动轴承振动监测与诊断［M］.北京:机械工业出版社,1995.

［2］王雷,张华良,余光伟.滚动轴承故障只能诊断系统［J］.轴承,2006(8):31-33.

［3］FRANK P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy a survey and some new results［J］.Automatic,1990,26(3):459-474.

［4］彭涛.基于传感器最优配置的故障检测方法研究［D］.长沙:中南大学,2005.

［5］PACHAUD C,SALVETAT R.Crest factor and kurtosis contributions to identify defects inducing periodical impulse forces［J］.Systems and Signal Processing,1997,11(6):903-916.

［6］PENG Tao,GUI Weihua,WU Min,et al.Fault diagnosis based on intelligent infor-mation processing technology［C］.Beijing:2002 IEEE Region 10 Conference on Computers,Communication,Control and Power Engineering,2002:1708-1712.

［7］VAPNIK V N.The nature of statistical learning theory［M］.New York:Springer Verlag,1995.

［8］李凌均,张周锁,何正嘉.支持向量机在机械故障诊断中的应用研究［J］.计算机工程与应用,2002(19):19-21.

［9］万书亭,佟海侠,董炳辉.基于最小二乘支持向量机的滚动轴承故障诊断［J］.震动、测试与诊断,2009,30(2):149-152.

［10］徐庆伶,汪西莉.一种基于支持向量机的半监督分类方法［J］.计算机技术与发展,2010,20(10):115-121.

［11］夏士雄,李佑文,周勇.一种半监督局部线性嵌入算法的文本分类方法［J］.计算机应用研究,2010,27(1):64-67.

［12］KRISTIN P B,AYHAN D.Semi-supervised support vector machines［C］.Cambridge:Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems,MIT Press,1998:368-374.

［13］JOACHIM S T.Making large-scale SVM learning practical［C］.Cambridge:Advances in Kernel Method-Support Vector Learning,1999:169-184.

［14］陈毅松,汪国平,董士梅.基于支持向量机的渐进直推式分类学习算法［J］.软件学报,2003,14(3):451-460.

［15］张健沛,赵莹,杨静.最小二乘支持向量机的半监督学习算法［J］.哈尔滨工程大学学报,2008,29(10):1088-1092.