Oscillation is a frequent type of control performance degradation. Usually, multiple oscillations simultaneously propagate through coupled control loops, bringing challenges to detection and isolation. An automatic oscillation analytics scheme is proposed that extracts oscillations before oscillation detection and isolation. Two variants of slow feature analysis (SFA), termed multi-lag SFA and multi-lag dynamic SFA, are proposed and compared to explore the time-lag effect and multi-lag autocorrelations. A novel isolation index is proposed to reveal the attenuation trend of oscillations from the energy viewpoint. One of the main advantages is that the proposed framework incorporates an oscillation extraction by using multi-lag dynamic SFA, greatly improving the performance for oscillation detection and isolation. The proposed method is also applicable to ascertain roots and travel paths in the presence of multiple oscillations, requiring little human supervision. Moreover, the framework is easy to implement, which shows its abilities, both in simulations and real industrial data.

振荡是控制性能下降的常见类型。通常，多个振荡会同时通过耦合的控制回路传播，这给检测和隔离带来了挑战。提出了一种自动振荡分析方案，该方案在振荡检测和隔离之前提取振荡。提出了慢特征分析（SFA）的两个变体，称为多时滞SFA和多时滞动态SFA，并进行了比较，以探讨时滞效应和多时滞自相关。提出了一种新的隔离指数，从能量的角度揭示了振荡的衰减趋势。主要优点之一是，提出的框架通过使用多延迟动态SFA合并了振荡提取，从而大大提高了振荡检测和隔离的性能。所提出的方法还适用于在存在多次振荡的情况下确定根和行进路径，几乎不需要人工监督。此外，该框架易于实施，在仿真和实际工业数据中均显示了其功能。