Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness

There are many models in the literature that have been proposed in the last decades aimed at assessing the reliability, availability and maintainability (RAM) of safety equipment, many of them with a focus on their use to assess the risk level of a technological system or to search for appropriate design and/or surveillance and maintenance policies in order to assure that an optimum level of RAM of safety systems is kept during all the plant operational life. This paper proposes a new approach for RAM modelling that accounts for equipment ageing and maintenance and testing effectiveness of equipment consisting of multiple items in an integrated manner. This model is then used to perform the simultaneous optimization of testing and maintenance for ageing equipment consisting of multiple items. An example of application is provided, which considers a simplified High Pressure Injection System (HPIS) of a typical Power Water Reactor (PWR). Basically, this system consists of motor driven pumps (MDP) and motor operated valves (MOV), where both types of components consists of two items each. These components present different failure and cause modes and behaviours, and they also undertake complex test and maintenance activities depending on the item involved. The results of the example of application demonstrate that the optimization algorithm provide the best solutions when the optimization problem is formulated and solved considering full flexibility in the implementation of testing and maintenance activities taking part of such an integrated RAM model.

The acceptable level of risk-taking for traditional rock climbers

Taking risk with all the consequences inevitably belongs to climbing. Each climber confronts his or her skills with the route he or she has chosen for an ascent. If the route is well protected, the rate of risk the climber takes is lower. If the route is less protected, the level of risk that the climber is exposed to proportionally increases. The aim of the research is to determine the level of risk-taking in traditional climbing on sandstone. We focus on how the level of risk affects climber’s performance and what reserve a climber needs to be able to cope with the higher risk and reduce it? The problem is solved by methods of quantitative research and the sample comprises more than 300 respondents. The results of the research prove a significant difference of climbers’ performance in dependence on rate of risk. Climbers usually reach lower performance according to the grading scale when climbing traditional routes with a higher level of risk.

Granger causality and systemic risk

Building on the concept of Granger causality in risk in Hong et al. (2009), and focusing on an international sample of large-capitalization banks, we test for predictability in comovements in the left tails of returns of individual banks and the global system. The main results show that large individual shocks (defined as balance-sheet contractions exceeding the 1% VaR level) are a strong predictor of subsequent shocks in the global system. This evidence is particularly strong for US banks with large desks of proprietary trading. Similarly, we document strong evidence of financial vulnerabilities (exposures) to systemic shocks in US subprime creditors.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.