The fault monitoring and diagnosi based on KPLS
In this paper, a novel fault monitoring and diagnosis approach based on kernel partial least squares(KPLS) is introduced. Unlike other nonlinear least squares (PLS) techniques, KPLS does not consider any nonlinear systems optimization procedures and has the characteristics similar to that of linear PLS. In this paper, KPLS provides good monitoring performance by finding those latent variables that present a nonlinear correlation with the response variables and at the same time improve model understanding. Simulation results show the proposed method can effectively capture the nonlinear relationship among variables and improve diagnosis performance.
kernel partial least squares(KPLS) fault monitoring and diagnosis model understanding
Yingwei Zhang Hongqiang Li
College of Information Science and Engineering, Northeastern University, 110004, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5299-5303
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）