International Journal of Reliability, Quality and Safety Engineering

The one-shot devices are highly reliable and, therefore, accelerated life tests are often employed to perform the experiments on such devices. Obviously, in the process, some covariates are introduced. This paper considers the proportional hazards model to observe the effect of covariates on the failure rates under the assumption of two commonly used models, namely the exponential and the Weibull for the lifetimes. The Bayes implementation is proposed using the hybridization of Gibbs and Metropolis algorithms that routinely extend to missing data situations as well. The entertained models are compared using the Bayesian and deviance information criteria and the expected posterior predictive loss criterion. Finally, the results based on two real data examples are given as an illustration.

Each system goes through several intermediate states between its initial state and the failure state, which are called degradation states. In addition, knowing these intermediate states allows us the building of an effective maintenance plan for the system under consideration. In this paper, we have considered a two-component parallel system where each component is subjected to two levels of degradation. Our first objective is to identify the mathematical (probabilistic) model associated with this system and this when the hypothesis of periodic inspection of the system is retained. Then, the main idea is to build the economic model encompassing the different average costs (inspection costs, preventive maintenance costs and corrective maintenance costs) spent during a renewal cycle. The latter will allow us to determine the optimal “inspection interval” while minimizing the average global-cost spent. Finally, in order to illustrate and analyze the sensitivity of the optimal inspection policy to the various maintenance costs, numerical examples are presented.

Process safety techniques have been used in industry for decades to make processes and systems safer and to optimize them, and thus to improve sustainability. Their main aim is to prevent damage to people, equipment and the environment.

In this overview, process safety and risk management techniques are shown that can be applied in the different life cycle phases of an application without much implementation effort. A broad and universal applicability in a wide range of business sectors is set as the main focus.

In addition to the application of system improvement techniques, a number of additional considerations, such as maintenance and the consideration of abnormal operating conditions, are included in order to be able to comprehensively improve a system or application.

Software reliability growth models (SRGMs) are widely used to estimate software reliability by analyzing failure dataset throughout the testing process. A large number of SRGMs have been proposed on a regular basis by researchers since the 1970s. They are represented with a set of assumptions and a set of parameters. One major problem in SRGMs is that the uncertainties surrounding the assumptions and parameters are generally not taken into account by most of them. Therefore, sometimes, the predicted reliability on testing phase significantly varies in actual operational phase. This paper presents a logistic growth model that incorporates a special parameter to consider the effects of all possible uncertainties. A systematic analysis is carried out to identify the major uncertain factors and their impacts on the fault detection rate. The applicability of the model is shown by validating it on two different real datasets that are commonly used in various studies. The comparisons with nine established models in terms of mean square error (MSE), variance, predictive-ratio risk (PRR), $R^2$and AIC have been presented.

Parameter estimation on heavily censored data is a challenging issue. This paper addresses this issue using a two-step single-parameter maximum likelihood method to estimate mean time to failure (MTTF) and Weibull shape parameter (WSP). The first step fits the data to three one-parameter auxiliary models, which are special cases of a two-parameter quasi-normal distribution with nonnegative support, to obtain three estimates of the MTTF. The second step estimates the WSP through fixing each of the MTTF estimates. The best estimates are selected from three pairs of estimates based on appropriate rules. A simple numerical method is also proposed to construct the confidence intervals of the estimated MTTF and WSP. The auxiliary models are derived, the model selection rules are specified, and a numerical experiment is carried out to illustrate the accuracy of the proposed method.

Researchers have proposed several software reliability growth models, many of which possess complex parametric forms. In practice, software reliability growth models should exhibit a balance between predictive accuracy and other statistical measures of goodness of fit, yet past studies have not always performed such balanced assessment. This paper proposes a framework for software reliability growth models possessing a bathtub-shaped fault detection rate and derives stable and efficient expectation conditional maximization algorithms to enable the fitting of these models. The stages of the bathtub are interpreted in the context of the software testing process. The illustrations compare multiple bathtub-shaped and reduced model forms, including classical models with respect to predictive and information theoretic measures. The results indicate that software reliability growth models possessing a bathtub-shaped fault detection rate outperformed classical models on both types of measures. The proposed framework and models may therefore be a practical compromise between model complexity and predictive accuracy.

Reliability is one of the key dimensions of the quality of services and products that should be always evaluated. Growth and development of industries can be achieved by appropriate reliability engineering of products. Companies should evaluate and predict the reliability of products and accordingly find and fix the potential problems. In this regard, early detection of reliability problems based on the parameters of the production line or quality test results can prevent future warranty costs. Early detection of reliability problems based on production process and test data has not gained much attention in the literature. Therefore, an early detection model for predicting the reliability of products according to their quality test results is proposed in this research. For this purpose, hot test and warranty data of car engines manufactured by an automotive company are utilized. This data are prepared to predict engine reliability after preprocessing and removing inefficient data. Then, engines are divided into two homogeneous clusters using particle swarm optimization (PSO) clustering algorithm. Afterwards, the data in these clusters are used to feed the Artificial Neural Network (ANN) to predict the reliability of the engines. The obtained results show that the proposed ANN-based method is able to predict the reliability of the engines based on engine kilometers operated and hot test results. Also, it is shown that the proposed method outperforms the Cox proportional hazards model which has previously been used for early detection of product reliability.