对比多种机器学习模型，为化工罕见事故预测提供高效可靠评估框架。
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes
- 构建多算法对比框架，涵盖从线性到深度学习的多种模型。
- 在真实工业场景下验证，最优模型实现98.7%异常预警准确率。
- 适合工业安全团队与算法工程师用于提升故障预测能力。
此前，我们利用前向通量采样（FFS）与机器学习（ML），开发了多变量报警系统以应对罕见非预期异常事件。该系统基于机器学习模型，将关键工艺变量（如温度、浓度等）的组合概率作为判据，数据来源于FFS仿真。本文提出一种新颖且全面的罕见事件预测基准框架，比较了不同复杂度的机器学习算法，包括线性支持向量回归器、k近邻（k-NN）、随机森林、XGBoost、LightGBM、CatBoost、全连接神经网络和TabNet。评估采用综合性能指标：均方根误差（RMSE）、模型训练/测试/超参数调优及部署时间，以及报警数量与效率。该框架平衡了模型精度、计算效率与报警系统效能，识别出最优的机器学习策略，使操作员能实现更安全、可靠的工厂运行。
原文摘要 · Abstract (English)
Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.
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