Study of similarity analysis methods for aviation system failures

Safety in tunnels is being contemplated through a holistic approach due to accidents that occurred through years. Technological innovations have led to the tunnel concept evolution from civil to technological infrastructure, where technology overcomes the limits of the geometric design, increasing its operating capacity. This paper aims to illustrate SCADRA principles (Supervisory Control Acquisition and Dynamic Risk Analysis), developed thanks to EURAM (EUropean Risk Analysis Method) methodology, which assessed the risk of over 600 road tunnel tubes, focusing on the following values: users and managers' needs (Human-centricity), usage of green technologies and application of energy saving strategy (Sustainability), and improvement of tunnels resilience (Resilience). SCADRA is used in monitoring continuously the tunnel state by gathering the safety influence variables and performing a dynamic risk analysis, quantitative and probabilistic, in real-time. The system was installed in three Italian road tunnels, executing over 130,000 instantaneous risk analyses. The applications’ results and the future developments will be presented according to tunnels’ safety improvement, maintenance, and management.

The article proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called supervisory control and dynamic risk analysis (SCADRA). SCADRA achieves operation and management benefits for safety, functionality, and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface) for real-time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements could not be realized.

The paper proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called SCADRA (Supervisory Control and Dynamic Risk Analysis). SCADRA achieves operation and management benefits for safety, functionality and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface HMI) for real time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements couldn't be realized.

Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model

The need to develop an integrated dynamic safety and risk analysis model for decommissioning offshore jacket structures is driven by the risky, expensive and complex nature of the operation. Many of the existing risk analysis techniques applicable to offshore assets failed to recognise and capture evolving risks during different stages of the decommissioning operation. This paper describes risk-based safety model to conduct quantitative risk analysis for offshore jacket decommissioning failure. First, a bow-tie technique is developed to model the accident cause-consequence relationship. Subsequently, a Bayesian belief network is used to update the failure probabilities of the contributing elements and thus, provides a more case-specific and realistic safety analysis when compared to the static nature of a bow-tie. This paper also presents the application of experiential learning in the dynamic safety analysis. The proposed technique is tested using a real-life case study from the Shell Brent Alpha platform. An algorithm to limit the effect of generic failure data was also developed. It is observed that the proposed technique helps to identify hazards shortly before they occur and sensitivity analysis revealed the most critical elements of the operation that must be managed to prevent catastrophe and consequently, reduce associated costs of remediation.

10 - Dynamic risk analysis and risk model evaluation

Dynamic simulation for risk analysis: Application to an exothermic reaction

This report concentrates on dynamic probabilistic risk analysis of optical elements for complex characterizationof damages using physical model of solid state lasers and predictable level of ionizing radiation and space weather.The following main subjects will be covered by our report: (a) a solid-state laser model; (b) mathematical modelsfor dynamic probabilistic risk assessment; and (c) software for modeling and prediction of ionizing radiation. Aprobabilistic risk assessment method for solid-state lasers is presented with consideration of some deterministicand stochastic factors. Probabilistic risk assessment is a comprehensive, structured, and logical analysis methodaimed at identifying and assessing risks in solid-state lasers for the purpose of cost-eectively improving theirsafety and performance. This method is based on the Conditional Value-at-Risk measure (CVaR) and theexpected loss exceeding Value-at-Risk (VaR). We propose a new dynamical-information approach for radiationdamage risk assessment of laser elements by cosmic radiation. Our approach includes the following steps: (a)lasermodeling, modeling of ionizing radiation in”uences on laser elements, (b) probabilistic risk assessment methods,and (c) risk minimization. For computer simulation of damage processes at microscopic and macroscopic levelsthe following methods are used: (a) statistical; (b) dynamical; (c) optimization; (d) acceleration modeling, and(e) mathematical modeling of laser functioning. Mathematical models of space ionizing radiation in”uence onlaser elements were developed for risk assessment in laser safety analysis. This is a so-called black box orinput-output model, which seeks only to reproduce the behaviour of the systems output in response to changesin its inputs. The model inputs are radiation in”uences on laser systems and output parameters are dynamicalcharacteristics of the solid laser.Keywords: risk analysis, laser elements, damage, global optimization, prediction

This article briefly reviews the theory of decision making under uncertainty and the programming methods used to consider uncertainty in farm planning. A multiperiod MOTAD model is developed to analyze the risk and return of a farm's investment financing, production, and marketing plans. The results of an empirical model of a beginning farmer in the Corn Belt are briefly presented to illustrate the potential applicability of the multiperiod MOTAD model and demonstrate the type of information generated. Most farm management decisions are made under conditions of uncertainty. This uncertainty in agriculture arises from market forces, weather, disease, insect damage, and other factors that cannot be controlled or accurately predicted. However, many agricultural economic models used to study farm management problems are specified under assumed certainty. For example, conventional linear programming used extensively in farm planning analyses does not accurately accommodate uncertainty. Of course, assumed yields and prices can be changed in these models to see how the farm organization is affected by changes in these variables. But there is no measurement of the risk associated with each farm organization. As a result, the conventional linear programming solutions for farm organization have often been rejected because, these solutions may specify actions that lead to a higher degree of risk than many farm managers are willing to accept [3, 5]. The objective of this article is to present a multiperiod linear programming model that can be used to evaluate farm management decisions under uncertainty. The discussion will begin with a review of the concepts of decision making under uncertainty. Then numerical analysis techniques will be discussed with emphasis on dynamic risk models. Finally, recent numerical results obtained from the use of a multiperiod MOTAD (Minimization of Total Absolute Deviations) model will be presented to illustrate its potential in dynamic risk analysis. DECISION MAKING UNDER UNCERTAINTY We assume that the objective of the rational individual is to maximize utility. Utility is derived from present and future consumption. Consumption, in turn, is a function of income. Utility can then be expressed as a function of income as:

The problem of risk update is one of the core problems of dynamic risk analysis. As for updating widely used probabilistic risk, it’s a kind of special problem of pattern recognition which means to transfer from old pattern to new pattern with new information. Aiming at updating probabilistic risk only with the new observations and without the original observations, this paper proposes the morphogenetic updating algorithm to update the probabilistic risk with only new observations and old risk values by reproducing the morphogenesis of new observations and old risk values based on morphogenetic estimation. The form rules hidden in the risk values and new observations are discovered by internal source in Write operation, and the projection of the input and the cosine similarity measure as the basis for defining the weight of updating are computed in Read operation. Based on Resconi theorem, the input is reproduced by its projection on the context which provides the same mathematical space for updating and stores the structural information hidden in the risk. By applying the algorithm to updating the probabilistic risk of typhoon rainstorms occurred in Guangdong Province, it proves that morphogenetic updating algorithm provides us a method to updating probabilistic risk fast and satisfactorily and it’s a sustainable updating method which can be used for dynamic risk analysis.

This chapter consists of four sections outlining how to deal with hazards and ongoing risk management. First, we elaborate on how to approach an analysis of a system’s hazards and implement and maintain a hazard log. Next, we elaborate on how ongoing risk management and dynamic risk analysis should ensure that high risk systems are under continuous monitoring and regular review. In the process of hazard identification and risk management, having clearly formulated risk tolerability criteria is paramount. Therefore, Sect. 12.3 outlines several approaches one could use for this purpose. When addressing a high risk system’s possible hazards and risk management strategies, it is important to also ensure the effective management and integration of the specific rules, limitations, and constraints that must be followed and monitored when using the system. The last section therefore outlines on safety-related application conditions (SRAC).

Applications of smart technologies for automatic near miss detection in the industrial safety

In the digital era, effective business management relies on dynamic risk analysis and real-time data integration, particularly amid the evolving landscape shaped by technological advancements and external factors such as climate change and global health crises. This study delves into the specific demands for digital services within the transportation sector, focusing on the crucial task of identifying an optimal data-driven management system (platform) to bolster transportation decision-making processes. The paper revolves around the formulation of a comprehensive conceptual framework for the development of intelligent services and platforms tailored explicitly to transport enterprises. Methodologically, a thorough analysis of critical infrastructure-related challenges was conducted, emphasizing the integration of a service-oriented approach to enhance overall functionality. Central to the paper’s approach is the careful navigation of conflicting user requirements, resource constraints, and the imperative of maintaining adaptability in service implementation. Additionally, a robust data flow analysis framework is presented, encompassing data collection, model building, and model extrapolation, which enables the generation of reliable outputs essential for informed decision-making. Notably, the study underscores the pivotal role played by the EN.I.R.I.S.S.T. research infrastructure in delivering essential services to the transportation domain, offering accessible data, user-friendly interfaces, and data analysis tools. The findings highlight the enthusiastic reception of the diverse services among potential users, indicating a strong willingness to engage and benefit from the proposed solutions. By emphasizing the integration of intelligent services, the paper seeks to present a systematic approach aimed at enhancing the efficiency, productivity, and competitive edge of transport enterprises through the strategic deployment of advanced technological solutions and proactive planning. This paper ultimately contributes cutting-edge research insights, empowering transportation managers, planners, and decision-makers with valuable resources for informed business intelligence and corporate strategy.

Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

The objective of this study is to carry out reliability and risk analyses of a methodology of dynamically applying Flood Control Level (FCL) within a constraint boundary for the Three Gorges Reservoir (TGR) in middle China. The dynamic application model is based on the mid-term inflow forecasts, with different dispatching rules developed for forthcoming inflows of different magnitudes. The reliability analyses of applying this model were conducted by simulating the model by forecasted inflows of 6 extreme flooding years. For the lack of real forecasting data, the so-called forecasted inflows were actually artificially generated by a stochastic model. The risks of applying such a dynamic application model were calculated by generating 9 999 design floods, for segmented 3 flooding stages in a flooding season, and for varying return periods. The obtained design floods were then routed through the dynamic application model, the exceedance frequencies of the results which violating the defined 3 risk events were calculated, and regarded as the risk rates. The results showed that the forecast-based dynamic application model is reliable for at least the routed 6 extreme years, therefore assumed to be also reliable for other normal hydrological years. The calculated risks of applying such a model are also much smaller compared to a research result accomplished previously. Therefore, both reliability and risk analyses indicate the applicability of the developed model under variational FLC conditions for the TGR.