Methodological developments for probabilistic risk analyses of socio-technical systems

International audienceNowadays, the risk analysis of critical systems cannot be focused only on a technical point of view. Indeed, several major accidents have changed this initial way of thinking. As a result, there exist numerous methods that allow to study risks by considering on the main system resources: the technical process, the operator constraining this process, and the organisation conditioning human actions. However, few works propose to jointly use these different methods to study risks in a global approach. In that way, this paper presents a methodology, which is under development between CRAN, EDF and INERIS, allowing an integration of these different methods to probabilistically estimate risks. This integration is based on unification and structuring knowledge concepts; and the quantitative aspect is achieved through the use of Bayesian Networks. An application of this methodology, on an industrial case, demonstrates its feasibility and concludes on model capacities, which are about the necessary consideration of the whole causes for a system weakness treatment, and the classification of these contributors considering their criticality for this system. This tool can thus be used to help decision makers to prioritise their actions

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems

Modeling and simulation of offshore workers' behavior

The offshore oil and gas industry functions in a team work culture in which operations depend not only on individuals’ competency, but also on team skills. Team skills are even more necessary when it comes to handling emergency conditions. Emergency conditions are dynamic in nature and personnel on board are challenged with evolving high-risk situations, time pressure, and uncertainty. One way to effectively handle emergencies is to train personnel to a competency level, both individually and as a part of a team. This would increase the chance of achieving safety in a timely manner using the available resources such as information, equipment, and people. Such training involves enhancing team members' understanding of human performance, in particular, the social and cognitive aspects of effective teamwork and good decision making. Post-accident analysis of offshore accidents shows that conventional training programs are often too generic, and that they are not designed to identify and tackle the human factors that are critical for evolving offshore emergency situations. Recognition of the importance of human factors on operator performance raises the need for training that goes beyond conventional training programs and incorporates non-technical training focusing on leadership, command, decision making, communication, and teamwork. A major difficulty to design such training is that it involves practicing emergency exercises with a potentially large number of participants, each playing the appropriate role in a given scenario. Such large-scale team exercises suffer from both organizational and educational drawbacks. The amount of human and financial resources needed for such a training exercise is difficult to organize. Furthermore, it is very hard, if not impossible, to get all team members together at the same time and location. Also, the team members may have variability in the competency levels (novice versus advanced trainees) and hence different training needs. One effective and flexible solution to this problem is to use intelligent artificial agents, or ‘virtual workers’, in a virtual environment (VE) to play different roles in the team. Virtual workers are artificially intelligent agents that can reproduce behaviors that are similar to or compatible with those of a real worker. This research proposes to develop a human behavior simulation model (HBM) that can be used to create such virtual workers in the context of offshore emergency egress. The goal of this research is to develop a human behavior model that can simulate offshore workers’ emergency response under the influence of performance influencing factors (PIFs). The first part of the work focuses on understanding human behavior during offshore emergency situations. A two level, three factor experiment was conducted in a virtual environment (VE) to investigate the relationships between the PIFs and human behavior. Influence of both internal and external PIFs were investigated. Knowledge acquisition and inference processes of individuals were also investigated in the experimental study. In the second part, a computational model was developed to capture the across-subject variability observed during the experiment. Interviews with subject matter experts (SME) were conducted at this step to ensure that the model is able to produce a realistic range of human behaviors. The final step was to validate the developed behavior model. All high-level tasks to validate the HBM were performed. Special emphasis was given on acceptability criteria testing to ensure that the integrated HBM performs adequately under different operating conditions

Process Resilience Analysis Framework for Design and Operations

Process plants are complex socio-technical systems that degrade gradually and change with advancing technology. This research deals with exploring and answering questions related to the uncertainties involved in the process systems, and their complexity. It aims to systematically integrate resilience in process design and operations through three different phases of prediction, survival, and recovery using a novel framework called Process Resilience Analysis Framework (PRAF). The analysis relies on simulation, data-driven models and optimization approach employing the resilience metrics developed in this research. In particular, an integrated method incorporating aspects of process operations, equipment maintenance, and process safety is developed for the following three phases: •Prediction: to find the feasible operating region under changing conditions using Bayesian approach, global sensitivity analysis, and robust simulation methods, •Survival: to determine optimal operations and maintenance strategies using simulation, Bayesian regression analysis, and optimization, and •Recovery: to develop a strategy for emergency barriers in abnormal situations using dynamic simulation, Bayesian analysis, and optimization. Examples of a batch reactor, and cooling tower operations process unit are used to illustrate the application of PRAF. The results demonstrate that PRAF is successful in capturing the interactions between the process operability characteristics, maintenance, and safety policy. The prediction phase analysis leads to good dynamic response and stability of operations. The survival phase helps in the reduction of unplanned shutdown and downtime. The recovery phase results in in reduced severity of consequences, and response time and overall enhanced recovery. Overall, PRAF achieves flexibility, controllability and reliability of the system, supports more informed decision-making and profitable process systems

Interpretive structural model of key performance indicators for sustainable manufacturing evaluation in automotive companies

This paper aims to analyze the interrelationships among the key performance indicators of sustainable manufacturing evaluation in automotive companies. The initial key performance indicators have been identified and derived from literature and were then validated by industry survey. Interpretive structural modeling (ISM) methodology is applied to develop a hierarchical structure of the key performance indicators in three levels. Of nine indicators, there are five unstable indicators which have both high driver and dependence power, thus requiring further attention. It is believed that the model can provide a better insight for automotive managers in assessing their sustainable manufacturing performance