Storage reliability modeling for hybrid systems with initial failures and masked data: An EM-LS approach

Abstract

Abstract Storage reliability is critical for systems that remain dormant throughout their lifecycle, particularly for mission-critical infrastructure such as radiation detection warning systems and various military defense applications. Typically, operational reliability is not always 100% at the onset of storage, and the exact causes of survival for a hybrid system are often unknown. This paper investigates a storage reliability model that accounts for potential initial failures and masked data from hybrid systems. To fully leverage inspection and masked data, a measure based on the least squares (LS) method, combined with an Expectation-Maximization (EM)-like algorithm, is proposed for hybrid systems. An LS-based parametric estimation procedure is developed to update the testing data, and the initial reliability and failure rates of the components in the hybrid system are subsequently estimated using the LS method. The effectiveness of the proposed methods and algorithms is verified through simulations using data from an exponential 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 hybrid systems is conducive to accurately evaluating product quality and preventing critical system failures. HIGHLIGHTS A combination approach to estimate the storage reliability of a hybrid system is proposed. The masked data from the components in the hybrid system is studied. The initial operational reliability not always being 1 in storage is presented. An LS-based EM-like algorithm is developed for mining the masked data from the hybrid system.