For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

中文翻译：

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基于核熵分量分析和多类支持向量机的相分割与识别-多相批处理故障诊断烟花算法

针对批处理过程中非线性和多阶段的特点，提出了一种自适应多阶段批处理过程故障诊断方法。首先利用核熵分量分析(KECA)方法自适应地实现多相分区，将过程数据映射到高维特征空间，然后构造核熵和角结构相似度。然后以角结构相似度为统计量，基于划分阶段和有效失效特征，通过KECA特征提取方法，建立了多阶段KECA失效监测模型。一种应用多类支持向量机（MSVM）和烟花算法（FWA）的多阶段批处理故障诊断方法，建议自动识别每个子相故障诊断。所提出的多阶段故障诊断方法的有效性和优点通过对补料分批青霉素发酵过程的案例研究来说明。