International Journal of Reliability, Quality and Safety Engineering

The abundance of online educational resources has made it increasingly difficult for students to identify the correct learning materials in recent years. Overcoming the information overload that has emerged in the new education systems is possible via a tailored recommendation system. It encourages students to look for new ways to get around the subject matter and to use information from all across the world. Because of this, many academics are working to create learning systems that incorporate methods for creating a unique learning experience for each user. Therefore, our proposed approach was to create an appropriate learning route for each student, and they are using Educational Resource Information Based on an Adaptable Genetic Algorithm(ERI-AGA). Evidence from studies shows that the suggested technique can provide relevant course materials for students based on the specific needs of students to help them study better in a Web-based system. Personal recommendation engine, pre-processing and learning-based model development, and implementation of the recommendation system will be researched. Participatory budgeting PB-level data storage and processing as well as the ability to suggest in real time will be studied. The capacity to make real-time suggestions and the storing and processing of PB-level data will be investigated. It was critical to check the system’s availability by running associated tasks and performance tests. The comparison values demonstrated that ERI-AGA was a reliable and accurate assessment procedure.

This paper presents a new four-parameter lifetime model by generalizing the Inverse Weibull (IW) distribution using Transmuted Kumaraswamy family of distributions. The proposed model explains the high probability at the tail effectively. The induction of additional parameters enhance the potentiality of the IW distribution and make it a pliant model that provides a vast range of existing distributions as special cases. Several probabilistic properties of the proposed model including moments, probability weighted moments, moment generating function, quantile function, reliability measures, and order statistics are discussed. The maximum likelihood (ML) procedure is adopted for the estimation of model parameters. The efficiency of ML estimates is to testify through simulation study. Four datasets from the field of reliability science are used to expound the competence of the proposed model.

In reliability studies, the data is often truncated and hence the residual random variable plays a vital role in the modelling and analysis of lifetime data. Like various reliability measures such as hazard rate, mean residual life function, variance residual life function, the residual coefficient of variation is also an important tool considered by many. In this paper, we study a quantile version of coefficient of variation of residual life, an alternative to the traditional distribution function measure. We study various properties of it and obtain characterizations based on some well-known probability models. We introduce a class of distributions with linear quantile-based residual coefficient of variation and study their basic reliability properties. We also obtain some stochastic comparison and aging properties using quantile-based residual coefficient of variation. The L-moment method of estimation for the class of distributions with linear quantile-based residual coefficient of variation has also been illustrated using two real data sets.

The paper introduces a new failure time distribution called additive Teissier–Weibull distribution. Among its features, it is capable of modeling increasing and bathtub hazard rates. It is thus useful in modeling heterogeneous populations and can be viewed as the failure time model of the system having two modes of failures that follow the Teissier and Weibull distributions, respectively. Some of its important properties are obtained, such as quantiles, moments and shapes of the probability density and hazard functions. Four different methods of estimation such as the maximum likelihood, least squares, weighted least squares and maximum product spacing are proposed to estimate the unknown parameters. To compare the performance of the estimates, an extensive simulation study is carried out with varying sample sizes. Finally, failure times of primary reactor pumps and power generators are fitted and analyzed to show that the new additive hazard rate model may give a better fit than many other existing models.

Employee training is essential for corporate activities to improve their production efficiency and product quality. The most representative two types of employee training are known, i.e., on-the-job training and off-the-job training. Off-the-job training can be classified into two types, i.e., compulsory training and non-compulsory training. Safety training and compliance are known as compulsory training, and they are needed to ensure that daily work continues without serious problems. Compulsory education is undertaken in a classroom every year by all employees of a department or section. Daily e-learning is effective for complementing and enhancing mandatory education and it helps employees to remember what they have learned during their annual education. In this paper, we discuss optimal employee safety education models that are complemented and enhanced by e-learning. The expected cost rate of education is expressed using the imperfect maintenance model, and the optimal policies that minimize it are discussed.

Software maintainability is a vital concern of organizations that develop and maintain large software products. The models that assess the maintainability of software systems at initial development stages play a significant role. In the Software Maintainability Prediction (SMP), a prevalent issue that needs to be taken care of is imbalanced data problem. For SMP, imbalanced data problem arises when the software classes that require high maintenance effort are less in number than classes that require low maintenance effort. In this paper, we dealt with the imbalanced data problem by the data resampling. With the imbalanced data, efficient machine learning algorithms are unable to predict the data points of both classes competently. Therefore, we examine the effectiveness of hybridized (HYB) techniques. The HYB techniques aid in finding an optimal solution for a problem by judging the goodness of multiple solutions. As per the results of the study, Adaptive synthetic minority oversampling technique (Adasyn) and Safe level synthetic minority oversampling technique (SafeSMOTE) are the best techniques of imbalanced data. Also, among the investigated HYB techniques, Fuzzy LogitBoost (GFS-LB) and Particle Swarm Optimization with Linear Discriminant Analysis (PSOLDA) emerged as the best techniques to predict maintainability.

This paper is a probabilistic performance study of a repairable complex system with three subsystems coupled in a series configuration. All three subsystems have been differently configured and also have different operational schemes. The first subsystem has $n$ units and works under the $k$-out-of-$n$:G scheme, the second subsystem has four identical units and associates with a defined work scheme 2-out-of-4:G, however, the third subsystem has two indistinguishable units and works under the 1-out-of-2:G policy. The units’ failure rates of the subsystems are different but constant and follow an exponential distribution, however, there are two types of repair facilities available for repair general and copula repair. Under the consideration of the system with the operational duration, the failed unit was replaced with a new one and it was assigned for repair. In the complete shutdown condition, a copula repair has been employed to repair the system. Traditional reliability measures have been studied for different values of failure and repair through supplementary variable and copula approaches. The numerical computations for securing different values of $k$ and $n$ have been exhibited and tables and graphs predict the future behavior of the system performance. In the conclusion section, the effect of reliability measures has been highlighted.