In the petroleum industry, equipments must be maintained properly to meet the adequate reliability standards in order to achieve the desired business goals in terms of productivity, safety and environmental protection. This article offers a new approach focused on risk analysis to select a better maintenance strategy. The proposed approach consists of three stages. In the first step, we identify the accident scenarios that could lead to the loss of production and damage to the environment. In the second step, we estimate the frequency of occurrence of these scenarios. In the third step, we calculate the economic losses and environmental taxes. Finally, an appropriate maintenance strategy is proposed, taking into account the evaluation results obtained by the previous steps. A case study illustrates the proposed approach and shows that the latter constitutes an important decision support tool to improve the existing maintenance strategy to comply with regulations and standards in term of productivity, reduction of costs and environmental protection.

The Safety Instrumented System (SIS) is an automated system used to implement one or more safety instrumented functions. A SIS, like the Emergency Shutdown (ESD) system, consists of any combination of sensor(s), safety PLC(s) and final element(s) (e.g. ESD valves). ESD valves are the last line of defense against risks, although the ESD valve has high performance, the data (based on expert judgment and OREDA database) indicates that ESD valves failures are the most critical in the ESD systems. In order to improve the reliability and safety of these valves, we applied the FMEDA diagnostic technique. We started with a decomposition of the ESD valve to the subsystems and we identified their functions. Then we described the failure modes, their mechanisms, their sites and their effects. Then we identified the impact of each failure mode according to the criticality classes and identified the failure rates and their class according to the criticality and the detectability by automatic diagnosis of each mode and from the failure rates we calculated the Safe Failure Fraction (SFF) and Safety Integrity Level (SIL) required and we concluded that the actuator subsystem is the most critical system. Finally, we proposed preventive and protective measures to eliminate or reduce the risk of failure.

In the petroleum industry, equipments must be maintained properly to meet the adequate reliability standards in order to achieve the desired business goals in terms of productivity, safety and environmental protection. This article offers a new approach focused on risk analysis to select a better maintenance strategy. The proposed approach consists of three stages. In the first step, we identify the accident scenarios that could lead to the loss of production and damage to the environment. In the second step, we estimate the frequency of occurrence of these scenarios. In the third step, we calculate the economic losses and environmental taxes. Finally, an appropriate maintenance strategy is proposed, taking into account the evaluation results obtained by the previous steps. A case study illustrates the proposed approach and shows that the latter constitutes an important decision support tool to improve the existing maintenance strategy to comply with regulations and standards in term of productivity, reduction of costs and environmental protection.

Workplace accidents (WAs) are and will remain a major concern for organizations’ managers. Their control requires putting in place a prevention strategy framed by several factors (human / social, economic, regulatory ...). The successful implementation of this strategy is conditioned on the ground, by the junction of three essential stages, namely: the analysis, evaluation and control of WAs. These three stages are interdependent where a successful control of an action plan is conditioned by a thorough assessment of an accident risk criticality. The latter depends on a good analysis of the accident. Indeed, a good analysis of WAs largely conditions their prevention strategy and that is why the analysis of WAs occupies a prominent place in such strategies. WAs analysis is conducted using appropriate models referred to as "WAs analysis models". Among those cited in the literature, we quote the model 24 that is a contemporary and more systematic model compared to other models. In this context that this article fits in, which aims to highlight its multiple contributions for the analysis of WAs.

The workplace, and more particularly the healthcare sector, has recently experienced a staggering increase in violence. These aggressive behaviors are resulting in considerable consequences on healthcare workers, both in terms of mental and physical health. In light of this observation, this study aims to provide a quantitative analysis of the potential causes leading to violence in Algerian hospitals, which have become the place where tensions arise, especially during these uncertain times caused by the COVID-19 pandemic. Therefore, we started with conducting a field survey, in order to highlight the main causes behind this violence, as well as the strategy in terms of how it is managed as a risk. Then we used ISHIKAWA diagrams to classify predefined causes into several categories and anticipate the likelihood of such violent behaviors. Finally, the results of this study revealed that working conditions were the main cause of violence in Algerian hospitals. In order to remedy this gap, we recommend improving the healthcare staff well-being, as well as prioritizing proactive measures preventing violent behaviors.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

The decision process is considered as the key element for the smooth behavior of systems. For non-complex or linear systems, this process usually follows clear rules or requirements, so that the decision will be easy to make without any difficulty. In the case of complex (non-linear) systems, the decision-making process is repeatedly challenged problems because of the interactions of several factors at the same time and because of the hazards characterizing these factors. This situation will worsen further if the system goes out of its normal operating range to fall into uncertainty. In this paper, we will try to make our contribution, to arrive at simplifying the decision-making process, while playing on human factors (ability, anticipation, risk-taking, etc.). As this process can be improved by the development of intelligent decision support tools.

Malaysian construction industry is considered as substantial power for the economy of the country. The value of construction work is increasing year per year. Malaysia's economy experienced a commendable growth the recent years; basically, the construction industry is statistically significant with economic development and has strong linkages with other industry sectors. However, the Malaysian construction industry is facing many challenges especially the fatal accidents. These last came first on top of the construction sector compared all other industries. This article describes the importance of the construction sector for Malaysia's economy and its efficient contribution across all states of Malaysia. Furthermore, detailed statistics and analysis were carried out to describe the severity of fatal accidents occurred in construction industry of Malaysia. Besides, recommendations were listed in order to better manage the construction industry without involving in fatal accidents. By taking into account the significant influence of the construction industry on Malaysian economy, full attention should be given to the construction field by the Malaysian government and improve further the construction management in general and the construction safety in particular.