Plant-wide oscillation detection and root cause diagnosis are important for maintaining control performance. Existing methods are mainly limited to detecting single and time-invariant plant-wide oscillations. In this paper, a data-driven model combining multivariate nonlinear chirp mode decomposition (MNCMD) with multivariate Granger causality (MGC) is proposed to detect and analyze root causes for multiple plant-wide oscillations in process control system. First, an MNCMD-based detector is developed to capture the multiple plant-wide oscillations, where oscillating variables caused by different sources are automatically clustered into various groups. Then, MGC is applied to each group to obtain the root causes of multiple plant-wide oscillations. Compared with state-of-the-art detection methods, the proposed approach shows better performance in the following aspects: (i) ability to extract both single/multiple plant-wide oscillations; (ii) capability to process both time-invariant/time-varying oscillations and provide accurate time-frequency information. This work also outperforms original Granger causality and nonlinearity index-based method in providing clearer causal network. The effectiveness and advantages of the proposed approach are demonstrated with the help of both simulation and industrial case studies.

全厂范围的振动检测和根本原因诊断对于维持控制性能很重要。现有方法主要限于检测单一且随时间变化的全厂范围的振荡。本文提出了一种将多元非线性线性调频模式分解（MNCMD）与多元格兰杰因果关系（MGC）相结合的数据驱动模型，以检测和分析过程控制系统中多个全厂范围振荡的根本原因。首先，开发了一种基于MNCMD的检测器来捕获多种全厂范围的振荡，其中由不同源引起的振荡变量会自动聚集到各个组中。然后，将MGC应用于每个组，以获得多个全厂范围振荡的根本原因。与最新的检测方法相比，所提出的方法在以下方面表现出更好的性能：（i）提取单个/多个全厂范围振荡的能力；（ii）处理时变/时变振荡并提供准确的时频信息的能力。这项工作在提供更清晰的因果网络方面也优于原始的Granger因果关系和基于非线性指标的方法。仿真和工业案例研究都证明了该方法的有效性和优势。这项工作在提供更清晰的因果网络方面也优于原始的Granger因果关系和基于非线性指标的方法。仿真和工业案例研究都证明了该方法的有效性和优势。这项工作在提供更清晰的因果网络方面也优于原始的Granger因果关系和基于非线性指标的方法。仿真和工业案例研究都证明了该方法的有效性和优势。