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.

多阶段特性已成为批处理过程的基本问题之一，并且已引入了几种阶段划分方法来监视过程。针对间歇过程的非高斯和非线性问题，本文提出了一种混合智能方法来监测多级工况。所提出的算法包括会聚级划分（CSD），多边界超球支持向量数据描述（MH-SVDD）和改进的bat算法（MBA）。利用CSD算法对数据进行处理并进行阶段划分，包括数据长度处理，三维展开和K-均值聚类。MH-SVDD算法是构造两个超球体，可以克服传统边界SVDD的不足。MH-SVDD的高斯核函数宽度参数在多级故障监测中起着非常重要的作用，建立了一种改进的bat算法来选择最优参数。描述了半导体刻蚀工艺的实验结果，结果表明，该模型在分批工艺的多阶段状态监测中具有较高的故障监测精度。