Conceptual Framework for Multi-State Systems in Smart Factories using Reliability-Redundancy Allocation

Abstract

In the era of Industry 4.0, smart factories integrate cyber-physical systems, IoT, and data analytics to enhance operational efficiency and resilience. Multi-state systems (MSS), where components exhibit multiple performance levels beyond binary failure states, are pivotal in modeling the dynamic reliability of these complex environments. This paper proposes a conceptual framework for reliability-redundancy allocation in MSS within smart factories, extending traditional models to incorporate non-exponential distributions, repairability, and real-time data-driven actions. Building on discrete event simulation and genetic algorithms, the framework addresses challenges like predictive maintenance and adaptive redundancy. Key elements include an updated characteristics triangle with a digital dimension, multi-objective optimization for availability and cost, and integration with digital twins. An illustrative example from a smart assembly line demonstrates the framework's applicability. The study highlights opportunities for enhanced system resilience and identifies future research directions in AI-enhanced optimization.