Abstract
Modern manufacturing systems should comply with high reliability and availability standards in order to guarantee efficient planning and optimization. Manufacturing systems including autonomous robots make the problem more challenging. By increasing the number of components in the autonomous manufacturing system and with respect to their intermittent interactions, a complex system is evolved. The reliability of such a system is significant due to high repair costs and failure downtime duration. Therefore, in this paper a reliability measurement framework is developed for a triple state autonomous robot system in an automated manufacturing environment. In this framework, an extension of the triangular probability distribution is proposed. In an implementation study, the effectiveness and computational efficiency of the proposed method is illustrated. In addition, an analysis on the failure rate using the maximum likelihood estimation is reported.
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