The possibility of addressing the problem of process troubleshooting and understanding by modelling common and distinctive sources of variation (factors or components) underlying two sets of measurements was explored in a real‐world industrial case study. The used strategy includes a novel approach to systematically detect the number of common and distinctive components. An extension of this strategy for the analysis of a larger number of data blocks, which allows the comparison of data from multiple processing units, is also discussed.

中文翻译：

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Divide et impera：多集数据分析中常见和独特的可变性如何解散如何有助于工业过程故障排除和理解

在现实世界的工业案例研究中，探索了通过对两组测量基础的共同且独特的变化源（因素或组件）建模来解决过程故障排除和理解问题的可能性。所使用的策略包括一种新颖的方法，可以系统地检测常见和独特组件的数量。还讨论了这种策略的扩展，用于分析大量数据块，从而可以比较来自多个处理单元的数据。