MNCMD-based Causality Analysis of Plant-wide Oscillations for Industrial Process Control System

In this paper, a data-driven model combining multivariate nonlinear chirp mode decomposition (MNCMD) with multivariate Granger causality (MGC) is proposed to analyze root causes for multiple plant-wide oscillations in process control system. Firstly, 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. The effectiveness and advantages of the proposed approach are demonstrated with the help of both simulation and industrial case studies.

I. Introduction

In process control system, units, elements and variables are crossed-linked with each other through different paths, such as information flow or material flow [1]. Once oscillations generated in several loops, they usually propagate to other loops and eventually lead to plant-wide oscillations [2]. These oscillations degrade the process control performance a lot, for example, they would waste raw materials and energy, increase production cost, reduce product quality [3], [4]. Therefore, it is very important to find out the oscillation sources within the process plant as soon as possible [5], [6].