Multimode processes monitoring method via multiple SVDD model

Modern industrial processes always have multiple operation modes. Besides, the variable in the single mode often obey complex data distribution which is a mix of Gaussian distribution and non-Gaussian distribution. Considering the problems of both multimode and complex data distribution, a new multimode processes monitoring method called multiple SVDD is proposed based on the local outlier probability algorithm and the support vector data description algorithm. First, given that the differences exist between different modes, the clustering is conducted by employing the differential strategy and the local outlier probability algorithm. Second, the SVDD algorithm is used to build the monitoring model in each single mode. And then, the most suitable model is selected for each testing sample through calculating the outlier probability. Finally, the feasibility and efficiency are proved through the Tennessee Eastman process simulation.