关键词: 局部线性嵌入算法, 支持向量数据描述, 故障检测

Abstract: As industrial processes become more complex, on-line monitoring of the processes are gaining importance for plant safety, maintenance, and product quality. To handle the nonlinear problem for process monitoring, a novel fault detection method was proposed by combining locally linear embedding (LLE)with support vector data description (SVDD).Firstly, LLE manifold learning algorithm was performed for nonlinear dimensionality reduction and thus the main feature of the collected data was extracted. Then, the mapping matrix from data space to feature space was calculated by using local linear regression which guaranteed the real-time property of the algorithm. Next, in order to avoid the influence of data noise on the traditional statistics, a fault detection model was established based on SVDD in the feature space, while a corresponding statistic and its control limit were determined at the same time. Finally, the feasibility and efficiency of the proposed method were shown through a numerical simulation and the Tennessee Eastman (TE)benchmark process.

Key words: locally linear embedding, support vector data description, fault detection