Inference on Constant Stress Accelerated Life Tests Under Exponentiated Exponential Distribution

Accelerated life tests have become increasingly important because of highercustomer expectations for better reliability, more complicated products withmore components, rapidly changing technologies advances, and the clear needfor rapid product development. Hence, accelerated life tests have been widelyused in manufacturing industries, particularly to obtain timely informationon the reliability. Maximum likelihood estimation is the starting point whenit comes to estimating the parameters of the model. In this paper, besides themethod of maximum likelihood, nine other frequentist estimation methodsare proposed to obtain the estimates of the exponentiated exponential distribution parameters under constant stress accelerated life testing. We considertwo parametric bootstrap confedence intervals based on different methods ofestimation. Furthermore, we use the different estimates to predict the shapeparameter and the reliability function of the distribution under the usualconditions. The performance of the ten proposed estimation methods isevaluated via an extensive simulation study. As an empirical illustration,the proposed estimation methods are applied to an accelerated life test dataset

Design of a New Synthetic Acceptance Sampling Plan

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry

A Control Chart for Exponentially Distributed Characteristics Using Modified Multiple Dependent State Sampling

In this paper, a t-control chart based on modified multiple dependent state sampling is proposed for monitoring processes that assume time between events following exponential distribution. The chart has double control limits and employs information from a previous sample and the current sample. The control chart coefficient “constants” are estimated by considering different values of the in-control average run lengths. The detection ability of the proposed control chart is found to be better than that of control charts based on multiple dependent state sampling in terms of average run lengths and the standard deviation of run lengths and better than generalized multiple dependent state sampling in terms of average run lengths. Case studies with real data are included as illustrative examples for the implementation of the proposed chart