Detecting the Causal Structure of Risk in Industrial Systems by Using Dynamic Bayesian Networks

Abstract

Our study deals with detecting the causal structure of risk in industrial systems. We focus on the prioritization of risks in the form of correlated events sequences. To improve existing prioritization methods, we propose a new methodology using Dynamic Bayesian networks (DBN). We explore a new user interface for industrial control systems and data acquisition, known as Supervisory Control and Data Acquisition (SCADA), to demonstrate the analysis method of risk causal structure. Our results show that: (1) the network of variables before and after the failure is represented by a limited and distinct number of factors;(2) the network of variables before and after the failure can be graphically represented dynamically in a user interface to assist in fault prevention and diagnosis;(3) variables related to the sequence of events at the time of failure can be used as a model to predict its occurrence, and find the main cause of it, thus making it possible to prioritize the requirements of the production system on the right variables to be monitored and manage in the event of a breakdown