This paper deals with the reliability modelling and performance evaluation of three configurations arranged in series–parallel. Configuration I consist of six units in which four are on operational while two are on standby. Configuration II consist of seven units with three of the units are on standby while the remaining four are on operation. Configuration III comprises of two subsystems C and D with three unit in each subsystem with a unit on standby. Units in each configuration provide 25 MW. The failure and repair times are assumed to be exponentially distributed. Through the transition diagram, system of first-order linear differential difference equations are derived for each configuration and are used to obtain the corresponding explicit expressions of system availability and mean time to failure. Cost–benefit analysis is examined and compared among the configuration through numerical examples to determine the optimal configuration and it was found to be configuration I. This study is important to system designers and developers, maintenance personnel, engineers and plant management in the suitable in designing and analysis of maintenance policy and processes and also the assessment of performance and the safety of the systems in general during and after the burn-in period.

It is widely recognized that simulators play an essential and extremely important role in training programs for nuclear power plants (NPPs). The full-scope training simulators provide such in-depth training tools for reactor operators. A full-scope simulator has been developed for research reactor, Dhruva at BARC. The details about the development activities are covered in this paper. Safety analysis and operational experience consistently indicate that human error is one of the key contributors to the risk of a severe accident in a nuclear reactor. The use of full-scope simulator in the training of operators is an essential element to reduce human error. This paper presents the core neutronics and thermal hydraulics of model of Dhruva reactor incorporated in the Dhruva simulator. The core neutronics module is based on point kinetics model which estimates the reactor power depending on the core reactivity. The simulator can reproduce the different operational scenario, i.e., reactor operation under equilibrium and non-equilibrium condition, xenon transients, loss of regulation incidents, etc. The simulator will also be used to study the role of human reliability in safety of nuclear plants. The simulator was so designed that over and above training of the plant personnel, it will be used for safety studies in the area of reactor physics and thermal hydraulics. The interface was designed to support the interaction of the operators with the simulator through virtual panels supported by touch screens with high-fidelity graphic displays. Important Postulated Initiating Events (PIEs) like Loss of Offsite Power, Loss of Coolant Accident, Loss of Regulation Incident, etc. have been modeled. This information will provide an insight to verify and validate the plant Emergency Operating Procedures (EOPs). The simulator will also be used for development of human factor models as well as the design of future Visual Display Unit based control rooms. This will also enable the operators to align themselves from the traditional panel-based Human–Machine Interface to personal computers.

The concept of the industrial internet of things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to estimate the reliability based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. This study reviews general approaches for the most important degradation-based reliability estimation models proposed by several researchers during last few decades. The most commonly applied deterministic and stochastic degradation models are reviewed in this study. Furthermore, a roadmap for adopting the degradation-based reliability estimation models based on the concept of the IIoT is explained in detail.

A system/unit is called working in normal environment if it is operating within prescribed conditions set by the manufacturer, otherwise the system is called working in abnormal environment. For example; a hydraulic machine having capacity uplifting the weight of 500 tons exceeds its capacity is termed as working in abnormal environment conditions. In this paper, profit analysis of a warm standby non-identical unit system with single server subject normal/abnormal environment conditions has been discussed. There is a single server who is allowed to do job in normal weather condition only and while performing its job it adopts FCFS (first come first serve) policy. The system model consists of two non-identical units—one is operative and the other kept as warm standby. The main unit may fail directly from normal mode, while failure of warm standby unit takes place owing to remaining unused for a longer period of time. The time taken for repair activity by the server follows negative exponential distribution. The expressions of various reliability measures are analyzed in steady state using semi-Markov process and regenerative point technique. Also, take the arbitrary values for the parameters (i.e., λ, μ, ϕ and θ) to delineate the behavior of some important performance measures to check the efficacy of the system model under such situations shown in the graphs.

Software testing-resource allocation plays a significant role in software project management. During the last two decades, a number of software-resource allocation problems have been formulated from various points of view, by taking account of several optimization criteria with constraints. First, we consider three optimization problems with constraints in a combination of expected total software cost, cumulative software testing effort and desired software reliability level. Especially, we focus on the operational profile of a software product and formulate rather different software testing-resource allocation problems from the existing ones. The optimization algorithms to determine the software testing resource for each software component are given by means of nonlinear programming. Next, we consider a statistical estimation problem of the optimal software testing-resource allocation, where a simulation-based parametric bootstrap method is applied to obtain not only the confidence interval, but also the higher moments of an estimator of the optimal software testing effort for each software component. In simulation experiments, we give an illustrative example on how to evaluate the uncertainty of the estimator of the optimal software testing-effort allocation.

This paper takes into account the estimation of system reliability of a multi-component stress–strength model of a s-out-of-k system, where both strength and stress are non-identical components and follow Weibull and Burr-III distributions, respectively. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach. We consider Bayes estimation under squared error loss function using conjugate priors for the parameters involved in the models. We propose Markov Chain Monte Carlo (MCMC) techniques to generate samples from the posterior distributions and in turn computing the Bayes estimator of the system reliability. Simulation study shows that Bayes estimator works better as compared to maximum likelihood estimator.

The main objective of this paper is to demonstrate the necessity of addressing all physical causes that lead to the transition of maintainable systems from positive to negative functionability state during their lives. Addressing the reliability characteristics of components and systems in isolation from the analysis of the impact of the natural environment on is not sufficient. Hence, results of the research performed in accordance with the Mirce-mechanics principles have shown the significant impact of cosmic radiation on the in-service behaviour of aviation systems. Due to the rapid advances in electronics technology and the unrelenting demand for increased avionics functionality, the complexity of avionics systems has risen exponentially. Hence, ever more advanced microprocessor and memory semiconductor devices are being used that exhibit an increased susceptibility to cosmic radiation phenomena. Single event effects have been the primary radiation concern for avionics since the late 1980’s when the phenomenon, which had previously only been observed in orbiting satellites, also began to appear in aircraft electronic systems. The trend with each new generation of avionics system is to use increasing quantities of semiconductor memories and other complex devices that are susceptible to decreases in reliability due to ionising radiation from the cosmic rays from space and alpha particles from radioactive impurities in the device itself. The interaction of this radiation can result in either a transient “soft error” effect such as a bit flip in memory or a voltage transient in logic, alternatively a “hard error” can be induced resulting in permanent damage such as the burnout of a transistor. Thus, this paper concludes that Mirce-mechanics approach to reliability is the only way forward for all members of the reliability community who wish to develop a method for accurate predictions of reliability, cost and effectiveness of aviation systems at early design stages, rather than to measure their in-service values and produce end of life statistics.

Humans are an integral part of complex systems such as nuclear power plants and have to play a significant role in ensuring the safety and reliability of these systems. Failure to perform the intended task within the stipulated time by the operator can challenge the safety of the system. Human reliability analysis (HRA) is a widely practiced methodology to estimate the contribution of operator error towards the overall risk to the facility. HRA methods quantify this contribution in terms of human error probability (HEP) accounting for various psychological and physiological factors that influence the performance of the operator. These factors are referred to as human factors (HF), which enhance or degrade the human performance. The paper discusses the use of virtual simulation as a tool to generate the HF data from the virtual model of an in-house experimental facility. This paper also demonstrates the use of multi-attribute utility theory to determine a suitable HRA method amongst several HRA methods to quantify the HEP based on the desired set of HRA attributes. As classical HRA methods, generally, do not address the interactions among the HFs, the Bayesian network technique has been employed in this study to account for HF interactions.

Software vulnerabilities pose potential security threats to a software. Researchers have been working on the problem of scheduling vulnerabilities and their fixation to minimize the potential risk they present. Vulnerability prioritization is an important step in this direction. Current work in this area focusses on implicit properties of a vulnerability and does not take into account the relative importance and their degree of interdependence. These factors contribute effectively to the decision of vulnerability fixation. In this paper, we consider these factors and propose multi-criteria decision-making (MCDM) techniques as a means of vulnerability prioritization. We have used three methods, namely normalized criteria distance (NCD), analytic hierarchy process (AHP), and decision-making trial and evaluation laboratory (DEMATEL) methodology for performing the task of vulnerability prioritization while treating different vulnerability types independent of each other in the first two approaches and showing interdependence in the third approach suggesting a cause–effect theory. We find the priorities of various vulnerability types using these three methods and suggest that the third approach is a more practical solution towards prioritization.