Key performance indicator (KPI)-relevant fault detection method has been raised for decades to hugely increase the economic interest of modern industries. However, the typical data-driven approaches like the kernel principal component analysis (KPCA) and the kernel entropy analysis (KECA) are inefficient to consider the influence taken by the fault factor on the KPI. Thus, in this work, an algorithm called the kernel entropy regression (KECR) is proposed to enhance the interpretability between the fault and the KPI. The proposed algorithm captures the information relevant to the KPI state in the subspace and rewords the decomposition of the KECA method. The angular structure of the KECR method achieves an accurate partition for process variables to hugely decrease false detection results. In the end, an industrial case is utilized to demonstrate the effectiveness of the KECR method.

中文翻译：

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基于核熵分量回归模型的工业系统关键绩效指标关联方法

与关键绩效指标 (KPI) 相关的故障检测方法已经提出了几十年，极大地提高了现代工业的经济利益。然而，核主成分分析（KPCA）和核熵分析（KECA）等典型的数据驱动方法在考虑故障因素对KPI的影响方面效率低下。因此，在这项工作中，提出了一种称为核熵回归（KECR）的算法来增强故障和 KPI 之间的可解释性。所提出的算法捕获与子空间中的 KPI 状态相关的信息，并改写 KECA 方法的分解。KECR 方法的角度结构实现了对过程变量的准确划分，从而大大减少了错误检测结果。到底，