Machinery Fault Diagnosis Using Improved LSSVM Method

摘要：

In fault diagnosis practice of recent years, the neural networks obtain many harvests, but it has a lot of questions in network structure selecting and network training. In the paper, the power spectrum of fault signals are decomposed by wavelet analysis, which predigests choosing method of fault eigenvectors, and a fault diagnosis model based on least squares support vector machine (LSSVM) is presented. Via structural risk minimization principle to enhance extensive ability, the model preferably solves many practical problems, such as small sample, non-linear, high dimension number and local minimum points. In the model, the nonsensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints. The parameter of kernel function is chosen on dynamic to enhance preciseness rate of diagnosis. The simulation results show the validity of the LSSVM model.

关键词： Wavelet packet analysis Fault diagnosis Least squares support vector machine

作者: Kuihe Yang Lingling Zhao

作者单位: Hebei University of Science and Technology,Shijiazhuang 050054,China;Ordnance Engineering College,Sh Hebei University of Science and Technology,Shijiazhuang 050054,China

会议类型: 国际会议

会议名称: 第二届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 武汉

会议语种:英文

在线出版日期: 2006-09-01（万方平台首次上网日期，不代表论文的发表时间）