Abstract

This paper presents a nature-inspired artificial Swarm Intelligence (SI) powered Digital Twin (DT)-based Bioscience-Inspired Maintenance Adaptation System (ABIMS) for improving resilience, sustainability and adaptability of Industry 4.0 environments. To enable decentralized, predictive and efficient maintenance processes, the proposed framework takes inspiration from collective intelligence and self-organization of biological systems in terms of power consumption. A digital twin of each physical asset is operating via an Internet-of-Things (IoT) connection and sensor data to synchronize the digital twin for estimation of residual lifetime, performance and fault prediction. Simultaneously, a biogenic-inspired hybrid optimization algorithm used by the intelligent swarm layer merges the Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) techniques for distributed resource allocation and maintenance task coordination. The trade-off between local optimization and global exploration that can be achieved by the hybrid approach, allows the system to provide high performance under uncertain industrial conditions. The results of the information system and decision theory predictions are integrated by an adaptive decision- making layer, that based on current operating conditions allows for on-the-fly tuning of maintenance strategies. The proposed system, based on what has been demonstrated by a simulation-based validation approach, shows significant improvements in the maintenance performance in terms of increased fault detection accuracy, reduced downtime and improved energy efficiency..

Keywords:Industry 4.0; Digital twin; Predictive maintenance; Artificial intelligence swarm; Bioscience-inspired optimization; Sustainability