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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe concept of the survival signature has recently attracted increasing attention for performing reliability analysis on systems with multiple types of components.It opens a new pathway for a structured approach with high computational efficiency based on a complete probabilistic description of the system. In practical applications, however, some of the parameters of the system might not be defined completely due to limited data, which implies the need to take imprecisions of component specifications into account. This paper presents a methodology to include explicitly the imprecision, which leads to upper and lower bounds of the survival function of the system. In addition, the approach introduces novel and efficient component importance measures. By implementing relative importance index of each component without or with imprecision, the most critical component in the system can be identified depending on the service time of the system. Simulation method based on survival signature is introduced to deal with imprecision within components, which is precise and efficient. Numerical example is presented to show the applicability of the approach for systems.

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This paper introduces an improved version of a novel inverse approach for the quantification of multivariate interval uncertainty for high dimensional models under scarce data availability. Furthermore, a conceptual and practical comparison of the method with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo is presented in the context of the DLR-AIRMOD test structure. First, it is shown that the proposed improvements of the inverse method alleviate the curse of dimensionality of the method with a factor up to 10 5. Furthermore, the comparison with the Bayesian results revealed that the selection of the most appropriate method depends largely on the desired information and availability of data. In case large amounts of data are available, and/or the analyst desires full (joint)-probabilistic

Recent major accidents in complex industrial systems, such as in oil & gas platforms and in the aviation industry, were deeply connected to human factors, leading to catastrophic consequences. A striking example would be the investigation report from the National Commission on the BP Deepwater Horizon Oil Spill and Offshore Drilling (2011) of the April 2010 blowout, in which eleven men died and almost five million barrels of oil were spilled in the Gulf of Mexico. The investigators unarguably emphasized the human factors role: features such a failure to interpret a pressure test and delay in react-*National Agency for Petroleum, Natural Gas and Biofuels (ANP), Brazil. ing to signals were found to have interacted with poor communication, lack of training and management problems to produce this terrible disaster. Other contemporary investigation reports, such as the Rio-Paris Flight 447 (Bureau d'Enquêtes et d'Analyses pour la sécurité de l'aviation civile, 2011) and Fukushima (Kurokawa, 2012), share the same characteristics regarding the significance of human-related features to the undesirable outcome. Thus, the understanding of the interactions between human factors, technology aspects and the organisational context seems to be vital, in order to ensure the safety of engineering systems and minimise the possibility of major accidents. A suitable Human Reliability Analysis (HRA) technique is usually applied to approach the human contribution to undesirable events.

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Structural complexity of systems, coupled with their multi-state characteristics, renders their reliability and availability evaluation difficult. Notwithstanding the emergence of various techniques dedicated to complex multi-state system analysis, simulation remains the only approach applicable to realistic systems. However, most simulation algorithms are either system specific or limited to simple systems since they require enumerating all possible system states, defining the cut-sets associated with each state and monitoring their occurrence. In addition to being extremely tedious for large complex systems, state enumeration and cut-set definition require a detailed understanding of the system's failure mechanism. In this paper, a simple and generally applicable simulation approach enhanced for multistate systems of any topology is presented. Here, each component is defined as a Semi-Markov stochastic process and via discrete-event simulation, the operation of the system is mimicked. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interiorpoint algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The principles and algorithms developed are applied to two numerical examples to demonstrate their applicability.