Journal of Statistics Applications & Probability

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The modern era is the age of science, technology and at the same time it is the age of competition. The advancement of new technology and increased global competition have emphasized the importance of product strength and reliability estimation. As a result, producers and manufacturers must now verify the strength and reliability of their products prior to releasing them to the market. In the past, reliability data analysis was a critical tool for this purpose. Traditionally, reliability data analysis entails quantifying these life characteristics through the examination of failure data. However, in many situations, obtaining such failure data has been extremely difficult, if not impossible, due to the length of time between designing and releasing a product, and the difficulty of designing a product that will last a long period due to its continuous use and operation. Faced with this challenge, reliability statisticians developed a technique called Accelerated Reliability Testing to rapidly determine the reliability and life characteristics of products. This technique increases product reliability and identifies when and how a product will fail in its intended environment. In the present work, we plan to investigate these mathematical reliability models to determine the costs associated with the various product guarantees. If component lifetimes follow the power-function distribution, the problem is examined under increasing stress using percent failure censoring. The method is referred as a process that applies accelerated testing to estimate the cost of age-replacement for goods sold under warranty. Additionally, a mathematical illustration is presented to illustrate the results.

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