Fault Identification of Nonlinear Processes

In this paper, a new kernel partial least-squares (KPLS)-based fault identification method is proposed. Although KPLS is superior to PLS in fault detecting of nonlinear processes, the fault identification methods for KPLS are limited. In this paper, the contributions are (1) The relationship between the input and the output variables are considered and each variable's contribution is measured using the gradient of kernel function. In the existing work, only input variables are concerned; (2) The complex computation is avoided since the new computation method of the partial derivative in the kernel matrix is introduced. The proposed method has two advantages: the ability to identify faulty variables in nonlinear process and guarantee correct diagnosis of simple sensor faults, compared with PLS using conventional contribution plots. In the end, case study on a numerical example and the electro-fused magnesia furnace (EFMF) is employed to illustrate the effectiveness of the proposed method, where the comparison with linear PLS method is involved as well.