This article aims to propose a model for analyzing work situations. In this context, we focus our interest on the simplification of procedures for working where the proposed model is used to analyze human behavior in response to non-compliance of work procedures. From this analysis, ways to improve work processes will be identified.

Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS.