An Algorithm for Storage Reliability Estimation of Parallel Systems With Masked Data and Initial Failures Under Weibull Lifetimes

ABSTRACT Storage reliability plays a critical role in systems that remain dormant throughout their entire life‐cycle, particularly for mission‐critical infrastructure such as radiation detection warning systems and various military defense applications. Typically, operational reliability is not always 1 at the onset of storage, and the exact causes of survival for a parallel system are often unknown. This paper investigates a storage reliability model that considers potential initial failures and masked data from parallel systems. To make full use of inspection and masked data, a measure based on the least squares (LS) method, combined with an Expectation‐Maximization (EM)‐like algorithm, is proposed for parallel systems. An LS‐based parametric estimation procedure is developed to update the testing data and the initial reliability and failure rates of the elements in the parallel system are subsequently estimated using the LS method. The effectiveness of the proposed methods and algorithms is verified through simulations using data from a Weibull lifetime distribution. The findings presented in this paper are expected to facilitate accurate evaluation of production reliability and identification of production quality. The technique for mining masked data from parallel systems is conducive to accurately evaluating product quality and preventing critical system failures.