Fault Detection Based on Modified Kernel Semi-Supervised Locally Linear Embedding

Abstract

In this paper, a novel approach to fault detection for nonlinear processes is proposed. It is based on a manifold learning called modified kernel semi-supervised local linear embedding. Local linear embedding (LLE) is widely applied to fault detection of complex industrial process. However, the LLE only preserves the local structure information of the sample, which ignores the global characteristics of the original data. The main contributions of the presented approach are as follows: 1) in order to utilize labeled data, the semi-supervised learning is introduced into LLE; 2) the regularization term is added to the calculation of local reconstruction weights matrix to strengthen the anti-noise ability in nonlinear processing; and 3) in order to extract the global and local characteristic of the observation data, the kernel principal component analysis objective function is integrated with the objective function of LLE. Experimental results on the production process of fused magnesia verify the performance of the proposed method.

Keywords

• Fault detection
• LLE
• KPCA
• semi-supervised learning