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Multi-state systems (MSS) are systems whose stochastic degradation process is characterised by several performance levels varying from nominal functioning to complete failure. MSS arise naturally in many application areas. MSS reliability evaluation and estimation has received much attention from researchers and a wide range of papers dealing with MSS have been published. In this paper, an approach based on Kronecker algebra combined with stochastic processes is proposed to evaluate the reliability of a series–parallel MSS. The main advantage of the proposed approach is that the mathematical expressions of the MSS reliability indices are derived from data of individual elementary components without generating the whole, possibly huge, MSS state space. Furthermore, the approach is well formalised and easy to implement thanks to Kronecker algebra operators. Examples are given to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

Highlights: [•] A probabilistic model on long-term degradation of tendon force is developed. [•] By using the model, we performed reliability evaluation of nuclear containment. [•] The analysis is also performed for the case with the strict maintenance programme. [•] We showed how to satisfy the target safety in the containments facing life extension. [Copyright &y& Elsevier]

A wireless sensor network (WSN) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN in order it works properly, mainly focusing on its reliability. From the system reliability perspective, it is important to take into account that WSN nodes are usually battery-powered and the WSN reliability strongly depends on the power management at node level. Since standby power management policies are often applied at node level and, moreover, interferences among nodes may arise, a WSN can be considered as a system affected by dynamic-dependent behaviors among its components and therefore, the dynamic reliability approach can be applied. Static-structural interactions are specified by the WSN topology. Active-sleep standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability dynamic reliability block diagrams are used in this paper. The proposed technique allows to overcome the limits of Markov models when considering nonlinear discharge processes, since such models cannot adequately represent the node aging process. In order to demonstrate the effectiveness of the DRBD technique in this context, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation. [ABSTRACT FROM AUTHOR]

This paper develops new models and concepts for incorporating the effect of protection system failures into power system reliability evaluation. The two types of protection failures, i.e., undesired-tripping mode and fail-to-operate mode, and their impact on reliability modeling are discussed. A complete Markov model for current-carrying components is established and its simplified form appropriately describes the overall reliability situation of individual components. Concepts of self-down state and induced-down state are introduced and used to build the composite unit model for quantitatively assessing the influence of protection failures on modeling system states. Finally, a Markov model of power systems with protection failures is proposed for system reliability evaluation. The proposed methodology is then illustrated in detail. [ABSTRACT FROM PUBLISHER]

Appropriate wind speed modeling is an important requirement in the reliability evaluation of wind integrated electric power systems. This paper examines and illustrates the effects on the generating capacity adequacy indices of a small test system of using different wind speed modeling procedures in Monte Carlo simulation and analytical reliability assessment. Wind speed data for a number of different time periods at two quite diverse locations in Canada are used in the studies. Wind speed is represented using hourly observed data, hourly mean wind speed data, autoregressive moving average (ARMA) time series, moving average (MA) time series, Normal distribution, and Markov chain models. [ABSTRACT FROM PUBLISHER]