Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies.

由于在交互式多回路环境中，在一个回路中产生的振荡可能传播到工厂的不同部分，因此，在整个工厂范围内进行振荡检测是维护大型工业控制系统的一项重要任务。在这种情况下，需要将由于共同原因而振荡的不同回路并因此将相似的频率分组在一起。本文提出了一种基于多元经验模态分解（MEMD）的自适应全厂振动检测方法以及分组算法。该方法可以识别不同变量之间以及仅具有随机噪声的变量之间的多个振荡组。所提出的方法还适用于非线性和非平稳时间序列，其中基于常规傅立叶分析的技术容易出错。在由于共同原因而振荡的每个组中，该方法还可以指示可能的振荡根本原因的位置。借助仿真和工业案例研究，确定了所提出方法的有效性。