Heuristic-Based Recommendation System for Dealing With Abnormal Situations in Industrial Applications

The effective management of process deviations and abnormal events depends on operational actions in providing the appropriate responses to each situation. Applications with requirements focused on the provision of resources tend to be handled by a human–machine interface (HMI) along with supervisory control and data acquisition (SCADA). Process reliability is achieved if the operation ensures that actions will be performed according to the required response and priority levels. This paper presents a novel architecture entitled the heuristic-based recommendation system (HB-RS). The main goal is to provide resources capable of streamlining the process of actively handling abnormal situations. For recommendation purposes, the use of probabilistic networks is proposed, which are well suited to represent uncertainty elements present in engineering applications. To create the inference mechanism for these systems, a multi-entity Bayesian network (MEBN) is proposed, highlighting the importance of semantic characterization via knowledge-based, probabilistic graphical model formalism, which is closely linked to the modeling paradigm in which we can predict situations. The developed capabilities have been applied to a real case study in the operation of a metro train system, and the results obtained indicate the value of the proposed method.