The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance for dynamic multimode chemical process monitoring (AKMD-t-SNE) is proposed to realize dynamic multimodal process monitoring. First, the augmented matrix strategy is utilized to ensure the sample contains the autocorrelation of the process. Moreover, AKMD-t-SNE method eliminates the scale and spatial distribution differences among multiple modes by calculating the kernel Mahalanobis distance between the samples to establish the global model. The features extracted via the proposed method are obviously non-Gaussian, and there will be a deviation in the construction of traditional statistics. Then, the support vector data description (SVDD) method is used to construct statistics to deal with this problem. In addition, a hybrid correlation coefficient method (HCC) is proposed to achieve fault isolation and improve the accuracy of isolation results. The advantages of the proposed scheme are illustrated by a numerical case and the Multimode Tennessee Eastman Process (MTEP) benchmark.

中文翻译：

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使用增强核马哈拉诺比斯距离的改进 t 分布随机邻域嵌入用于动态多模式化学过程监测

传统的数据驱动的过程监控方法可能不适用于具有动态和多模式特征的系统。在本文中，提出了一种名为改进的 t 分布随机邻域嵌入的新方案，该方案使用增强的马氏距离进行动态多模式化学过程监测（AKMD-t-SNE），以实现动态多模式过程监测。首先，利用增广矩阵策略确保样本包含过程的自相关。此外，AKMD-t-SNE该方法通过计算样本间的核马哈拉诺比斯距离建立全局模型，消除了多种模态之间的尺度和空间分布差异。所提方法提取的特征具有明显的非高斯性，与传统统计的构建存在偏差。然后，使用支持向量数据描述（SVDD）方法构造统计来处理这个问题。此外，还提出了一种混合相关系数法（HCC）来实现故障隔离，提高隔离结果的准确性。所提出方案的优点通过数值案例和多模田纳西伊士曼过程 (MTEP)基准进行说明。