Data-Driven Causal Analysis and its application on a Large-scale Board Machine

In large-scale chemical processes, disturbances can easily propagate through the process units and thereby adversely affect the overall process performance. In recent years, causal analysis has played a key role in the diagnosis of plant-wide disturbances. Causal analysis enables to identify the propagation path of the disturbance and thereby disclose the root cause. Data-driven causal analysis utilizes historical process data in the form of time series and examines to what extent the time series influence each other. If directionality between time series can be inferred, it is taken as evidence for a cause-and–effect relationship. Data-driven causal analysis can efficiently complement knowledge-based causal analysis and provide valuable insights on process dynamics with minimal efforts. The aim of this study is to apply several time and frequency domain data-driven causal analyses on an industrial case study of a paper board machine and to evaluate the effectiveness of each method. The analyses are applied on the drying section of a board machine due to its importance in the board making process and the high share of faults associated with this section. The outcome of each method is a causal model in the form of a directed graph describing the interactions among the variables in the process. The results of each method are discussed and methods are evaluated and compared using process knowledge. In addition, root cause analysis based on the frequency domain analysis is successfully applied.