Bagging support vector data description model for batch process monitoring

• An ensemble form of the traditional SVDD model is proposed.
• The new developed bagging SVDD model is used for batch process monitoring.
• Two ensemble strategies are proposed for monitoring results combination.
• Detailed results of different monitoring strategies are compared through an industrial process.

To improve the monitoring performance of the support vector data description model (SVDD), an ensemble form of SVDD is developed, which is termed as bagging SVDD in this paper. While different kinds of ensemble learning approaches have been developed in the past years, bagging is probably the most traditional and simplest one. By randomly selecting subsets from the original dataset, bagging constructs an individual SVDD model for each of these subsets. For practical utilization, the results of different individual SVDD models are ensembled/combined together. In this paper, two kinds of combination strategies are proposed, named as voting-based strategy and Bayesian-based strategy. Compared to a single SVDD model, the monitoring performance can be improved by the bagging SVDD method in most cases. The feasibility and effectiveness of the proposed method are demonstrated by an industrial semiconductor etch process.