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Abstract
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Long testing times are usually required for the life testing of very reliable products or materials. The testing process can be hastened by using accelerated life tests (ALTs). The lifespan of the items that ALTs inspect are reduced since they test products in more severe circumstances than those found in regular use scenarios. Data that was censored and disclosed the precise timings of failure may point to ALTs where all units assigned to test are unknown or where all units assigned to test have not failed for a few reasons. Including challenges with technology, tools, costs, and schedules. The step stress partially accelerated life test (SSPALT) was examined in this work using the type-I pro-gressive hybrid censoring scheme (type-I PHCS) and the type-II progressive censoring scheme (type-II PCS). The influence of the stress shift is explained using the tampered random variable (TRV) model, where the failure times of the items are assumed to follow the alpha power Lomax (APL) distribution. The unknown parameters are esti-mated using the maximum likelihood estimation (MLE) method. The asymptotic theory of MLE is also employed in the construction of the approximate confidence intervals (ACIs). While the point estimates under two censoring schemes are compared in terms of relative absolute biases (RABs) and root mean squared errors (RMSEs), ACIs are compared in terms of their lengths and coverage probabilities. Additionally, three possible optimal test strategies are investigated using different optimal criteria. The performance of the estimators was evaluated and contrasted with two censoring techniques with various sample sizes using a simulation study. Finally, a numerical example for insulating fluid between electrodes data is presented to illustrate how the methods will work in real-world scenarios.
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