Truncated Life Test Plans for Economic Reliability Based on Four-Parametric Burr Distribution

Burr distribution is considered as a probability model for the lifetime of products. Reliability test plans are those sampling plans in which items from a lot are put to test to make conclusions on the estimate of life, and hence acceptance or rejection of the submitted lot is done. A test plan designs the termination time of the experiment and the termination number for a given sample size and producer’s risk. Tables and graphs were provided for certain specific values of designs, and it is useful to verify the optimum reliability test plan realized by Burr distributions. 1. Introduction Reliability study plays a vital role in the quality control, and it can save time and money by realizing early conclusions. If a genuine product (reaching defined life) is rejected on the basis of sample information it is called Type-1 error. On the other hand, if an ingenuine product (not reaching defined life) is accepted by the consumer, then it is called Type-2 error. The decision to accept or reject a lot is subjected to the risks associated with these errors. The procedure is termed as “reliability test plan” or “acceptance sampling based on life test” [1]. Tailed probability distributions are the basis of reliability test plans. These distributions are used to find the reliability sampling plans which will be more economical for the experimenter. Kantam developed a detailed study on life tests based on log-logistic distribution [2]. Rosaiah and Kantam used Inverse Rayleigh distribution to present acceptance sampling [3]. Kantam et al. introduced economic reliability test plan for log-logistic distribution [4], and Aslam and Shahbaz considered generalized exponential distribution to explain economic reliability test plan [5]. Aslam also presented economic reliability test for a generalized Rayleigh Distribution [6]. Rao et al. explained the economic reliability test plan on the basis of Marshall-Olkin extended exponential distribution [7]. Rao et al. considered reliability test plans for type-II exponentiated log-logistic distribution [8]. Mugahal et al. introduced economic reliability group acceptance sampling plans for lifetimes, a Marshall-Olkin extended distribution [9]. Aslam et al. considered generalized exponential distribution to explain time truncated acceptance sampling [10]. It is found that a null hypothesis about scale parameter such as “the scale parameter is greater than or equal to a specified value” is equivalent to establish that the “average life of a product realized by a given scaled density exceeds the specified average life.” Acceptance of