Multistage Condition Monitoring of Batch Process Based on Multi-boundary Hypersphere SVDD with Modified Bat Algorithm

Abstract

Multistage characteristic has become one of the essential issues of batch process and several stage division approaches have been introduced to monitor the process. As the non-Gaussian and nonlinear problems of batch process, a hybrid intelligent method is developed to monitor the multistage conditions in this paper. The proposed algorithm includes converged stage division (CSD), multi-boundary hypersphere support vector data description (MH-SVDD), and modified bat algorithm (MBA). CSD algorithm is utilized to process the data and make the stage division, which consists of data length processing, three-dimension unfolding, and K-means clustering. MH-SVDD algorithm is to construct two hyperspheres, which can overcome the deficiency of traditional boundary SVDD. The Gaussian kernel function width parameter of MH-SVDD plays a very significant role in multistage fault monitoring, a modified bat algorithm is established to select the optimal parameter. The experimental of the semiconductor etching process is described, and the results demonstrate that the proposed model can gain higher fault monitoring accuracy in multistage condition monitoring of the batch process.