Abstract In the modern hot strip mill process (HSMP), the operating performance may deteriorate because of wear of equipment, mode transitions, and random disturbances. If the process is not adjusted and maintained, faults may occur, resulting in greater economic losses and potential safety hazards. Therefore, it is of great practical significance to carry out comprehensive operating performance assessment. In this paper, a lifecycle operating performance assessment framework based on robust kernel canonical variable analysis (RKCVA) is proposed to deal with automation hierarchy, nonlinearity, and outliers in the plant-wide HSMP. First, the HSMP is divided into upstream, midstream, and downstream in real-time control level (L1). Then, based on kernel canonical variable analysis (KCVA) and partial robust M-regression (PRM), the RKCVA models are developed for each stream and process control level (L2). Based on the Bayesian inference, statistical fusion is implemented to judge whether the process is in normal or faulty operating condition. After that, according to different evaluation rules of different operating conditions, the lifecycle operating performance assessment is realized. Finally, the framework is illustrated with a case study on a real HSMP. The assessment results show that the accuracy of the RKCVA is more than 10% higher than that of the KCVA.

中文翻译：

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基于鲁棒核典型变量分析的热带钢轧机生命周期运行性能评估框架

摘要 在现代热轧带钢工艺（HSMP）中，由于设备磨损、模式转换和随机扰动，运行性能可能会恶化。如果不对工艺进行调整和维护，可能会出现故障，造成更大的经济损失和安全隐患。因此，开展综合经营绩效考核具有重要的现实意义。在本文中，提出了一种基于鲁棒核规范变量分析 (RKCVA) 的生命周期运行绩效评估框架，以处理全厂 HSMP 中的自动化层次、非线性和异常值。首先，HSMP在实时控制级别（L1）上分为上游、中游和下游。然后，基于核典型变量分析（KCVA）和偏鲁棒 M 回归（PRM），RKCVA 模型是为每个流和过程控制级别 (L2) 开发的。基于贝叶斯推理，通过统计融合来判断过程是否处于正常或故障运行状态。之后，根据不同运行工况的不同评价规则，实现全生命周期运行性能评价。最后，通过对真实 HSMP 的案例研究来说明该框架。评估结果表明，RKCVA的准确率比KCVA高10%以上。实现全生命周期运行绩效评估。最后，通过对真实 HSMP 的案例研究来说明该框架。评估结果表明，RKCVA的准确率比KCVA高10%以上。实现全生命周期运行绩效评估。最后，通过对真实 HSMP 的案例研究来说明该框架。评估结果表明，RKCVA的准确率比KCVA高10%以上。