International Journal of Reliability, Quality and Safety Engineering

Most systems have multiple inputs that comprise of a mixture of excitations and component parameters. Excitations are different from component parameters in that they are always functions of time. In mechanical systems, these include applied forces, applied displacements, system settings, systems configurations and operating conditions. It would be convenient to include multiple excitations and multiple component parameters in a meta-model to take advantage of the inherent computation speed needed for timely probability-based design optimization.

In the development of the meta-model in this paper, we treat the component parameters in the same manner as the excitations and thus, in both cases, form time-sampled vectors. A design-of-experiments training regime creates a single input matrix, and using the mechanistic model, a single output matrix. Finally, a simple, explicit, meta-model is developed that turns an arbitrary vector of contiguous multiple excitations and multiple component parameters into the corresponding output vector (herein, the response). The approach provides an appealing and efficient solution to the multiple, mixed input problem, and in addition, requires only off-the-shelf computer software. The efficacy of the meta-model is shown through probability-based design optimization (PBDO) of a tire-wheel assembly, modelled as a mass-spring-damper system with nonlinear hysteresis, under a combination of practical inputs.

This paper presents Bayesian reliability sampling plans for the Weibull distribution based on progressively Type-II censored data with binomial removals. In constructing sampling plans, the decision theoretic approach is used. A dependent bivariate nonconjugate prior is employed. The total cost of the sampling plan consists of sampling, time-consuming, rejection, and acceptance costs. The decision rule is based on the Bayes estimator of the survival function. Lindley’s approximation is used to obtain Bayes estimates of the survival function under the quadratic and LINEX loss functions. However, the poor performance of Lindley’s approximation with small sample sizes can be observed. The Metropolis-within-Gibbs Markov Chain Monte Carlo (MCMC) algorithm show significantly improved performance compared to Lindley’s approximation. We use simulation studies to evaluate the Bayes risk and determine the optimal sampling plans for different sample sizes, observed number of failures, binomial removal probabilities and minimum acceptable reliability.

In parallel and distributed systems, multistage interconnection network (MIN) plays an important role for its efficient communication between processor and memory at a very low cost. A major class of MIN called Gamma network is known for its redundant network topology and is being used in broadband communication systems. The increased redundancy incorporation makes these networks more complex and hence reliability evaluation becomes complex. The performance evaluation of these network topologies requires reliability evaluation utilizing routing mechanism or techniques. In this paper, we have proposed four topologies of Gamma-Minus network using MUX and DEMUX. Terminal Reliability (TR), fault tolerance and routing schemes of Gamma-Minus network topologies proposed have been computed by utilizing different connection patterns of MUX/DEMUX. Also, performance indices such as TR, Reliability Cost Ratio (RCR), Fault Tolerance, etc. computed for different Gamma-Minus architectures have been compared with the existing Gamma networks, other than Gamma-Minus. All the performance indices for different Gamma-Minus topologies show improvement over the performance indices of different Gamma networks. The proposed Gamma-Minus architectures have been compared among themselves and also Gamma-Minus2 shows the best performance for all performance indices. To the best of our knowledge, most of the researchers have not compared fault tolerance and RCR performance measure.

We discuss a method of software dependability assessment based on stochastic differential equation modeling in order to consider the interesting factors of the numbers of components, cloud applications and users. Then, we consider the determination of an optimum software maintenance time, minimizing the total expected software cost. In particular, we develop the three-dimensional AIR application for reliability and cost optimization analyses based on the proposed method. Moreover, the three-dimensional application based on NW.js, known as the latest technology, is developed in this paper. Furthermore, we show several numerical examples of the developed application software to evaluate the performance of software optimization and reliability assessment tools for the big data on cloud computing.

Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed.

Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.