International Journal of Reliability, Quality and Safety Engineering

This paper provides Bayesian and classical inference of Stress–Strength reliability parameter, $R=P[Y<X]$, where both $X$ and $Y$ are independently distributed as 3-parameter generalized linear failure rate (GLFR) random variables with different parameters. Due to importance of stress–strength models in various fields of engineering, we here address the maximum likelihood estimator (MLE) of $R$ and the corresponding interval estimate using some efficient numerical methods. The Bayes estimates of R are derived, considering squared error loss functions. Because the Bayes estimates could not be expressed in closed forms, we employ a Markov Chain Monte Carlo procedure to calculate approximate Bayes estimates. To evaluate the performances of different estimators, extensive simulations are implemented and also real datasets are analyzed.

Software play remarkable roles in different critical applications. On the other hand, due to the shrinking of transistor size and reduction in supply voltage, radiation-induced transient errors (soft errors) have become an important source of computer systems failure. As the rate of transient hardware faults increases, researchers have investigated software techniques to control these faults. Performance overhead is the main drawback of software-implemented methods like recovery blocks that use technical redundancy. Enhancing the software reliability against soft errors by utilizing inherently error masking (invulnerable) programming structures is the main goal of this study. During the programming phase and at the source code level, programmers can select different storage classes such as automatic, global, static and register for the data into their program without paying attention to their inherent reliability. In this study, the inherent effects of these storage classes on the program reliability are investigated. Extensive series of profiling and fault-injection experiments were performed on the set of benchmark programs implemented with different storage classes. Regarding the results of experiments, we find that the programs implemented with automatic storage classes have inherently higher reliability than the programs with static and register storage classes without performance overhead. This finding enables the programmers to develop highly reliable programs without technical redundancy and performance overhead.

Performance of a software is an important feature to determine the quality of the software developed. Performance testing of modular software is a time consuming and costly task. Several performance testing tools (PTTs) are available in the market which help software developers to test their software performance. In this paper, we propose an integrated multiobjective optimization model for evaluation and selection of best-fit PTT for modular software system. The total performance tool cost is minimized and the fitness evaluation score of the PTTs is maximized. The fitness evaluation of PTT is done based on various attributes by making use of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model allows the software developers to select the number of PTTs as per their requirement. The individual performance of the modules is considered based on some performance properties. The reusability constraints are considered, as a PTT can be used in the same module to test different properties and/or it can be used in different modules to test same or different performance properties. A real-world case study from the domain of enterprise resource planning (ERP) is used to show the working of the suggested optimization model.

Due to the unavailability of complete data in various circumstances in biological, epidemiological, and medical studies, the analysis of censored data is very common among practitioners. But the analysis of bivariate censored data is not a regular mechanism because it is not necessary to always have independent data. Observed and unobserved covariates affect the variables under study. So, heterogeneity is present in the data. Ignoring observed and unobserved covariates may have objectionable consequences. But it is not easy to find that whether there is any effect of the unobserved covariate or not. Shared frailty models are the viable choice to counter such scenarios. However, due to certain restrictions such as the identifiability condition and the requirement that their Laplace transform exists, finding a frailty distribution can be difficult. As a result, in this paper, we introduce a new frailty distribution generalized Lindley (GL) for reversed hazard rate (RHR) setup that outperforms the gamma frailty distribution. So, our main motive is to establish a new frailty distribution under the RHR setup. By assuming exponential Gumbel (EG) and generalized inverted exponential (GIE) baseline distributions, we propose a new class of shared frailty models based on RHR. We estimate the parameters in these frailty models and use the Bayesian paradigm of the Markov Chain Monte Carlo (MCMC) technique. Model selection criteria have been performed for the comparison of models. We analyze Australian twin data and suggest a better model.

Roads have always been the main source of transportation all over the world. Easy accessibility and more safety are the most important features of road transportation. Improvements in these areas are constantly required and invited. Solar road studs are one of the remarkable improvements in road safety. Solar road studs use solar energy, which is the most sustainable and pollution-free source of energy that provides reliable power supplies and fuel diversification. Solar road studs are flashing solar cell-powered LED lighting devices used in road construction to delineate road edges and centerlines. This research work is dedicated to evaluating the reliability measures which include availability, mean time to failure (MTTF), cost analysis, and sensitivity analysis with their graphical representation by using the Markov process. Along with reliability assessment, Particle Swarm Optimization (PSO) technique is applied to optimize the cost of the system.

The underground high-voltage power transmission cables are high value engineering assets that suffer from multiple deteriorations through-out life cycles. Recent studies identified a new failure mode – the pitting corrosion deterioration on the layer of phosphor bronze reinforcing tape, which protects the oil-filled power transmission cables from oil leakage due to deterioration of the leads heath. Two models estimating the phosphor bronze tape life were established separately in this study. The first model, based on mathematical fitting, is generated using a replacement priority model from the power supply industry. This is considered as an empirical-based model. The second model, based on the corrosion fatigue mechanism, utilizes the information of the pit depth distribution and the concept of pit-to-crack transfer probability. The Bayesian inference approach is the conjunction algorithm to update the existing probability of failure (PoF) model with the newly identified failure modes. Through this algorithm, the integrated PoF model contains a more comprehensive background information while maintaining the empirical knowledge on the engineering assets’ performance.

Safety integrity level (SIL)-based functional safety assessment is widely required in designing safety functions and checking their validity of electrical/electronic/programmable electronic (E/E/PE) safety-related systems after being issued IEC 61508 in 2010. For the hardware of E/E/PE safety-related systems, quantitative functional safety assessment based on target failure measures is needed for deciding or allocating the level of SIL. On the other hand, IEC 61508 does not provide any quantitative safety assessment method for allocating SIL for the software of E/E/PE safety-related systems because the software failure is treated as a systematic failure in IEC 61508. We discuss the needfulness of quantitative safety assessment for software of E/E/PE safety-related systems and propose mathematical fundamentals for conducting quantitative SIL-based safety assessment for the software of E/E/PE safety-related systems by applying the notion of software reliability modeling and assessment technologies. We show numerical examples for explaining how to use our approaches.