关键词: 间歇过程, 故障检测, 非线性, 高斯与非高斯混合分布, 差分策略

Abstract: Aiming at the problem of bad fault detection effect of batch process because of its non-linearity and mixed distribution of Gaussian and non-Gaussian,Multi-way Differencial Neighborhood Preserving Embedding-Weighted and Differencial Independent Component Analysis (MDNPE-WDICA) algorithm for fault detection of batch process was proposed.The original data space was divided into Gaussian and non-Gaussian subspaces by Jarque-Bera testmethod (J-B test).In Gaussian subspace,MDNPE algorithm was proposed by combining differential strategy with NPE algorithm to preserve the local structure invariant and deal with the nonlinearity of data while the dimension of data was reduced,which could overcome the computational complexity caused by the introduction of the kernel function.In non-Gaussian subspace,WDICA algorithm was proposed by combining weighted differential strategy with ICA algorithm to solve the nonlinearity of data while the non-Gaussian information of data was fully extracted,and the local information of data was effectively used.A new monitoring statistic was established by Bayesian inference to realize fault detection for the whole batch process.The simulation results of penicillin production process demonstrated that the proposed algorithm was feasible and effective.

Key words: batch process, fault detection, nonlinear, Gaussian and non-Gaussian, differential strategy