Key performance indicators (KPIs), such as product quality variables or critical parameters in major units, play a crucial role in ensuring the desired performances in industrial processes. Nonetheless, focusing solely on monitoring process variables may result in the generation of nuisance alarms in response to disturbances that do not have a significant or meaningful impact on KPI variables. In this article, a novel KPI-related process monitoring method based on slow feature extraction and elastic net orthonormal subspace analysis (SFENOSA) is proposed. Traditional orthonormal subspace analysis (OSA) divides process data and KPI data subspaces into three orthonormal subspaces using least squares. To deal with the overfitting problem in high-dimensional space and enhance the robustness caused by correlated variables, the elastic net orthonormal subspace analysis (ENOSA) is developed by employing elastic net regularization in the OSA. Furthermore, to address the dynamic characteristics inherent in industrial processes, the slow feature analysis is naturally integrated into the framework of ENOSA for KPI-related process monitoring. Specifically, using the slow features extracted from process variables as the input and the KPI variables as the output, an ENOSA model is built. Based on the developed SFENOSA model, several monitoring statistics are established for KPI-related process monitoring. Experimental results on a numerical example, the well-known Tennessee Eastman process, and a real blast furnace ironmaking process demonstrate the superior performance of the proposed SFENOSA compared to the related methods.

中文翻译：

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SFENOSA：一种基于慢速特征提取和弹性网络正交子空间分析的 KPI 相关流程监控新方法


关键绩效指标 (KPI)，例如产品质量变量或主要单元的关键参数，在确保工业过程中实现所需性能方面发挥着至关重要的作用。尽管如此，仅关注监控过程变量可能会导致生成干扰警报，以响应对 KPI 变量没有重大或有意义的影响的干扰。本文提出了一种基于慢速特征提取和弹性网络正交子空间分析（SFENOSA）的新型 KPI 相关过程监控方法。传统的正交子空间分析 (OSA) 使用最小二乘法将过程数据和 KPI 数据子空间划分为三个正交子空间。为了解决高维空间中的过拟合问题并增强相关变量引起的鲁棒性，弹性网正交子空间分析（ENOSA）通过在OSA中采用弹性网正则化来发展。此外，为了解决工业过程固有的动态特性，慢速特征分析自然地集成到ENOSA的框架中，用于KPI相关的过程监控。具体来说，使用从过程变量中提取的慢速特征作为输入，以KPI变量作为输出，构建ENOSA模型。基于开发的SFENOSA模型，建立了多种监控统计数据，用于KPI相关的过程监控。数值示例、著名的田纳西州伊士曼工艺和真实高炉炼铁工艺的实验结果表明，与相关方法相比，所提出的 SFENOSA 具有优越的性能。