This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark data set which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the pre-aligned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.

本文提出了一种用于批处理过程监控的新型数据驱动框架，这是实现安全操作性并最大程度地降低生产率和利润损失的一项重要任务。我们使用基于非线性支持向量机的特征选择算法来开发高维过程数据，我们的目标是检索最有用的过程测量值，以进行准确，同时的故障检测和诊断。所提出的框架适用于广泛的基准数据集，其中包括描述22,200个批次和15个故障的过程数据。我们通过三种不同的时间跨度方法在预先对齐的批处理数据轨迹上训练故障模型和特定于时间的模型：一步滚动，两步滚动和演化，这会改变建模过程中的数据合并量。结果表明，两步滚动和演化时间范围方法的效果优于其他方法。无论采用哪种方法，提出的框架都为批处理过程的在线同时故障检测和诊断提供了有希望的决策支持工具。