Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Diagnosis
Locality preserving projection (LPP) is a newly emerging fault diagnosis method which can discover local manifold structure of the analyzed data set, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault diagnosis. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
nonlinear locality preserving projection kernel trick sparse model fault diagnosis
Deng Xiaogang Tian Xuemin
College of Information and Control Engineering, China University of Petroleum, Qingdao 266555, China
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）