用于大规模混合数据集的自动化批量蒸馏过程模拟以实现深度异常检测
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
AI总结：
本文通过结合实验数据与模拟数据构建混合数据集，实现批量蒸馏过程的自动化模拟，为深度异常检测提供新的数据基础。
摘要：
基于深度学习的化学过程异常检测具有显著潜力，但需要大量、多样且标注良好的训练数据，这些数据通常难以从工业操作中获得。本文引入了一个大规模的完全标注实验数据集，用于正常和异常操作条件下的批量蒸馏。在本研究中，我们通过添加相应的模拟数据集扩展该数据集，创建了一个新的混合数据集。模拟数据是通过自动化工作流生成的，使用了一个基于Python的过程模拟器，该模拟器采用定制的索引减少策略来处理底层微分代数方程。利用实验数据库丰富的元数据和结构化异常标注，实验记录被自动转换为模拟场景。在校准到单个参考实验后，其他实验的动力学被良好预测。这使得能够全自动、一致地生成大量实验运行的时间序列数据，涵盖正常操作和广泛的执行器和控制相关异常。所得到的混合数据集已公开发布。从过程模拟的角度来看，这项工作展示了使用批量蒸馏作为例子对大规模实验活动进行自动化、一致的模拟。从数据驱动的异常检测角度来看，混合数据集为模拟到实验风格迁移、伪实验数据生成以及未来化学过程监控中深度异常检测方法的研究提供了独特基础。
英文摘要：
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rarely available from industrial operations. In a recent work, we introduced a large, fully annotated experimental dataset for batch distillation under normal and anomalous operating conditions. In the present study, we augment this dataset with a corresponding simulation dataset, creating a novel hybrid dataset. The simulation data is generated in an automated workflow with a novel Python-based process simulator that employs a tailored index-reduction strategy for the underlying differential-algebraic equations. Leveraging the rich metadata and structured anomaly annotations of the experimental database, experimental records are automatically translated into simulation scenarios. After calibration to a single reference experiment, the dynamics of the other experiments are well predicted. This enabled the fully automated, consistent generation of time-series data for a large number of experimental runs, covering both normal operation and a wide range of actuator- and control-related anomalies. The resulting hybrid dataset is released openly. From a process simulation perspective, this work demonstrates the automated, consistent simulation of large-scale experimental campaigns, using batch distillation as an example. From a data-driven AD perspective, the hybrid dataset provides a unique basis for simulation-to-experiment style transfer, the generation of pseudo-experimental data, and future research on deep AD methods in chemical process monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
