Improved Fault Diagnosis in Online Process Monitoring of Complex Networked Processes: a Data-Driven Approach

Many of the fault detection and diagnosis frameworks currently used in complex industrial processes rely on the application of data-driven models. Among these methodologies, those based on principal component analysis (PCA) are particularly relevant due to its effectiveness in describing the normal operation conditions (NOC) in a parsimonious way, with resort to a reduced set of latent variables. However. PCA models are non-causal by nature and therefore fail to extract the intrinsic structure of the relationships between the variables, leading to limited fault diagnosis capabilities. To circumvent this limitation, we propose to implement a data-driven pre-processing module that codifies the causal structure of data and that can be easily plugged-in into current monitoring schemes. This pre-processing module makes use of a Sensitivity Enhancing Transformation (SET) that decorrelates the variables based on their causal structure, inferred through partial correlations. Therefore, deviations on the new decor-related variables represent specific changes in the process structure, making fault diagnosis more transparent. To demonstrate the applicability of the proposed approach, two case studies are considered (CSTR and the Tennessee Eastman process). The results show that mapping the causal structure by means of the SET leads to a set of variables directly linked with the true source of the fault, providing a simple and effective way to improve fault detection and diagnosis.