Abstract A multivariate statistical process monitoring scheme should be able to describe multimodal data. Multimodality typically arises in process data due to varying production regimes. Moreover, multimodality may influence how easy it is for process operators to interpret the monitoring results. To address these challenges, this paper proposes an on-line monitoring framework for anomaly detection where an anomaly may either indicate a fault occurring and developing in the process or the process moving to a new operating mode. The framework incorporates the Dirichlet process, which is an unsupervised clustering method, and kernel principal component analysis with a new kernel specialized for multimode data. A monitoring model is trained using the data obtained from several healthy operating modes. When on-line, if a new healthy operating mode is confirmed by an operator, the monitoring model is updated using data collected in the new mode. Implementation issues of this framework, including the parameter tuning for the kernel and the selection of anomaly indicators, are also discussed. A bivariate numerical simulation is used to demonstrate the performance of anomaly detection of the monitoring model. The ability of this framework in model updating and anomaly detection in new operating modes is shown on data from an industrial-scale process using the PRONTO benchmark dataset. The examples will also demonstrate the industrial applicability of the proposed framework.

中文翻译：

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一种用于监测具有多种操作模式的非线性过程的在线框架

摘要 多元统计过程监控方案应该能够描述多模态数据。由于不同的生产方式，过程数据中通常会出现多模态。此外，多模态可能会影响过程操作员解读监测结果的难易程度。为了应对这些挑战，本文提出了一种用于异常检测的在线监控框架，其中异常可能表明过程中发生和发展或过程正在转向新的操作模式。该框架将 Dirichlet 过程（一种无监督的聚类方法）和内核主成分分析与专门用于多模数据的新内核相结合。使用从几种健康运行模式获得的数据训练监控模型。上线时，如果操作员确认新的健康操作模式，则使用在新模式中收集的数据更新监控模型。还讨论了该框架的实现问题，包括内核的参数调整和异常指标的选择。使用双变量数值模拟来证明监测模型的异常检测性能。该框架在新操作模式下的模型更新和异常检测能力通过使用 PRONTO 基准数据集的工业规模过程数据显示。这些示例还将展示所提议框架的工业适用性。还讨论了内核的参数调整和异常指标的选择。使用双变量数值模拟来演示监控模型的异常检测性能。该框架在新操作模式下的模型更新和异常检测能力通过使用 PRONTO 基准数据集的工业规模过程的数据显示。这些示例还将展示所提议框架的工业适用性。还讨论了内核的参数调整和异常指标的选择。使用双变量数值模拟来证明监测模型的异常检测性能。该框架在新操作模式下的模型更新和异常检测能力通过使用 PRONTO 基准数据集的工业规模过程数据显示。这些示例还将展示所提议框架的工业适用性。