Osama H. Arif

An attribute control chart for a Weibull distribution under accelerated hybrid censoring

In this article, an attribute control chart has been proposed using the accelerated hybrid censoring logic for the monitoring of defective items whose life follows a Weibull distribution. The product can be tested by introducing the acceleration factor based on different pressurized conditions such as stress, load, strain, temperature, etc. The control limits are derived based on the binomial distribution, but the fraction defective is expressed only through the shape parameter, the acceleration factor and the test duration constant. Tables of the average run lengths have been generated for different process parameters to assess the performance of the proposed control chart. Simulation studies have been performed for the practical use, where the proposed chart is compared with the Shewhart np chart for demonstration of the detection power of a process shift.

A Control Chart for COM–Poisson Distribution Using Multiple Dependent State Sampling

In this article, an attribute control chart has been proposed for the COM–Poisson distributed non-conformities using multiple dependent states sampling based on the exponentially weighted moving average statistic. Average run lengths of the out-ofcontrol process with different shift levels of the COM–Poisson distribution have been calculated through simulation. From comparison, it has been observed that the proposed control chart is better in detecting the out-of-control process quickly as compared with the existing control chart. A simulated example has been given for the practical use of the proposed control chart. Copyright

Estimation of Reliability in Multicomponent Stress-Strength Based on two Parameter Exponentiated Weibull Distribution

ABSTRACT In this research article, we estimate the multicomponent stress–strength reliability of a system when strength and stress variates are drawn from an exponentiated Weibull distribution with different shape parameters α and β, and common shape and scale parameters γ and λ, respectively. We estimate the parameters by using maximum likelihood estimation (MLE) and hence the estimate of reliability obtained applying the MLE method of estimation when samples are drawn from stress and strength distributions. The small sample comparison of the reliability estimates is made through Monte Carlo simulation.

Log-logistic distribution for survival data analysis using MCMC

This paper focuses on the application of Markov Chain Monte Carlo (MCMC) technique for estimating the parameters of log-logistic (LL) distribution which is dependent on a complete sample. To find Bayesian estimates for the parameters of the LL model OpenBUGS—established software for Bayesian analysis based on MCMC technique, is employed. It is presumed that samples for independent non informative set of priors for estimating LL parameters are drawn from posterior density function. A proposed module was developed and incorporated in OpenBUGS to estimate the Bayes estimators of the LL distribution. It is shown that statistically consistent parameter estimates and their respective credible intervals can be constructed through the use of OpenBUGS. Finally comparison of maximum likelihood estimate and Bayes estimates is carried out using three plots. Additively through this research it is established that computationally MCMC technique can be effortlessly put into practice. Elaborate procedure for applying MCMC, to estimate parameters of LL model, is demonstrated by making use of real survival data relating to bladder cancer patients.

A new variable sample size control chart using MDS sampling

ABSTRACT In this manuscript, a new variable sample size (VSS) control chart using multiple dependent state sampling is proposed. The sample size for the next subgroup is chosen on the basis of the location of the control statistic from the current subgroup in two pairs of the control limits. The average run length of the proposed chart is evaluated using Monte Carlo simulation. The performance of the proposed chart over the existing VSS control chart is presented using simulation study.

An Attribute Control Chart Based on the Birnbaum-Saunders Distribution Using Repetitive Sampling

In this paper, an attribute control chart using repetitive sampling is proposed when the lifetime of a product follows the Birnbaum–Saunders distribution. The number of failures is to be monitored by designing two pairs of upper and lower control limits. The necessary measurements are derived to assess the average run length (ARL). The various tables for ARLs are presented when the scale parameter and/or the shape parameter are shifted. The efficiency of the proposed control chart is compared with an existing chart. The proposed chart is shown to be more efficient than an existing control chart in terms of ARL. A real example is given for illustration purpose.

Testing of Grouped Product for the Weibull Distribution Using Neutrosophic Statistics

Parts manufacturers use sudden death testing to reduce the testing time of experiments. The sudden death testing plan in the literature can only be applied when all observations of failure time/parameters are crisp. In practice however, it is noted that not all measurements of continuous variables are precise. Therefore, the existing sudden death test plan can be applied if failure data/or parameters are imprecise, incomplete, and fuzzy. The classical statistics have the special case of neutrosophic statistics when there are no fuzzy observations/parameters. The neutrosophic fuzzy statistics can be applied for the testing of manufacturing parts when observations are imprecise, incomplete and fuzzy. In this paper, we will design an original neutrosophic fuzzy sudden death testing plan for the inspection/testing of the electronic product or parts manufacturing. We will assume that the lifetime of the product follows the neutrosophic fuzzy Weibull distribution. The neutrosophic fuzzy operating function will be given and used to determine the neutrosophic fuzzy plan parameters through a neutrosophic fuzzy optimization problem. The results of the proposed neutrosophic fuzzy death testing plan will be implemented with the aid of an example.

Fourth-order kernel method for simple linear degradation model

ABSTRACT Degradation analysis is a useful technique when life tests result in few or even no failures. The degradation measurements are recorded over time and the estimation of time-to-failure distribution plays a vital role in degradation analysis. The parametric method to estimate the time-to-failure distribution assumed a specific parametric model with known shape for the random effects parameter. To avoid any assumption about the model shape, a nonparametric method can be used. In this paper, we suggest to use the nonparametric fourth-order kernel method to estimate the time-to-failure distribution and its percentiles for the simple linear degradation model. The performances of the proposed method are investigated and compared with the classical kernel; maximum likelihood and ordinary least squares methods via simulation technique. The numerical results show the good performance of the fourth-order kernel method and demonstrate its superiority over the parametric method when there is no information about the shape of the random effect parameter distribution.

A New Control Chart for Monitoring Reliability Using Sudden Death Testing Under Weibull Distribution

In this paper, a new control chart using sudden death testing is designed by assuming that the lifetime/failure time of the product follows the Weibull distribution. The structure of the proposed chart is presented. The control chart coefficient is determined using some specified average run length for the in control process and the shifted process. Simulation study is given for the illustration purpose.