http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/29 2026-04-07T06:05:30Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/422 A New Neutrosophic Model using Dus-Weibull Transformation with Application Unnipillai, Nayana; Anakha, K K; Aslam, Muhammad; Chacko, V. M.; Albassam, Mohammed There is a need to comprehend real-world problems that are marked by ambiguity and inflexibility. By taking into account the indeterminacies and inconsistencies, DUS transformation has been taken to Neutrosophic Weibull distribution and DUSNeutrosophic Weibull distribution is proposed. The probability density function is unimodal and decreasing in nature. Several statistical properties have been studied. The parameters of the proposed distribution are estimated using the maximum likelihood method. The proposed distribution has been validated on a real data set. The estimates are found to be more accurate than the classical distributions 2022-02-14T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/416 Joint Importance Measures for Repairable Multistate Systems Chacko, V. M.; Sania, Ann; Amrutha, M. In order to identify the vulnerable components whose joint effect would have changed system performance and ensure the required reliability of various multistate systems, joint importance measures of relevant components are used in the early design of systems. Due to the complexity of multistate systems that have the properties of non-linearity, uncertainty, and randomness, which make it difficult to analyze the reasons of failure mechanisms, model the system, estimate its reliability, and evaluate the joint importance measures of its components. This paper discussed measures of joint importance of three components for repairable multistate systems based on the classical Birnbaum measure. Eight importance measures are studied in detail. These joint importance measures provide a time-dependent analysis of the relevancy of components, thus adding insights on the contributions of the joint effect of three components on the system reliability or performance over time. An illustrative example is given. The results of the study show that joint importance measures can be a valuable decision-support tool for designers and engineers in the design of systems. 2023-12-14T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/415 Power Generalized DUS Transformation of Inverse Kumaraswamy Distribution and Stress-Strength Analysis Amrutha, M; Chacko, V. M Reliability analysis, including stress-strength analysis, for given data is more widely used in the reliability literature. A large number of new distributions are available, but many of them are not showing a good fit for the data under consideration. This inspires a researcher to introduce new lifetime distributions that demonstrate superior fitness in comparison to the existing distributions. So that more accurate reliability estimates can be obtained for the given data. The DUS transformation technique is widely used in reliability literature to create better models. Power generalized DUS(PGDUS) transformation to lifetime distributions, which is found to be useful to introduce more appropriate flexible distributions for the given data. Vinyl chloride data obtained from clean upgrading and monitoring wells in mg/L have been analyzed using DUS inverse Kumaraswamy (DUS IK), inverse Kumaraswamy (IK), and Weibull distributions. As a substitute for these distributions, this paper presents a new lifetime distribution employing PGDUS transformation, utilizing the inverse Kumaraswamy distribution as the baseline. The statistical properties of the proposed distribution are derived. The parameters of the proposed distribution are estimated using the maximum likelihood (ML) method, maximum product spacing (MPS), method of moment, and method of least squares. Additionally, Bayesian parameter estimates are acquired utilizing Lindley’s approximation and the Metropolis-Hastings algorithm. The consistency of the model is verified using mean squared error (MSE) and biases, which are obtained based on simulated values. Then, the proposed distribution is compared with the DUS-IK, IK, and Weibull distributions. In this paper, single-component and multi-component stress-strength reliability analyses are also conducted. 2024-03-30T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/401 ESTIMATION OF STRESS STRENGTH RELIABILITY USING PRANAV DISTRIBUTION Lukose, Ankitha; Chacko, V M This paper deals with the estimation of stress strength reliability parameter R, which is the probability of Y less than X when X and Y are two independent distribution with different scale parameter and same shape parameter. The maximum likelihood method is used to find an estimator for R. We also obtain the asymptotic distribution of the maximum likelihood estimator of R. Based on this asymptotic distribution, the asymptotic confidence interval can be obtained. We also propose bootstrap confidence interval for the parameter R. Analysis of a simulated data and a real life data have been presented for illustrative purposes 2023-12-01T00:00:00Z