Spectral-Adaptive Consensus Algorithm for Robust Fault Mitigation in Decentralized Smart Manufacturing Networks

Decentralized smart manufacturing systems serve as a robust foundation for optimizing production processes and maintaining stringent quality standards in modern industrial environments. However, in such distributed architectures, uncontrolled and rapid fault propagation presents significant challenges, often resulting in widespread operational disruptions and compromised system integrity. To address these issues, we propose the spectral-adaptive consensus fault mitigation algorithm (SAC-FMA), which efficiently detects, contains, and mitigates fault propagation across decentralized manufacturing networks. Our approach uniquely combines spectral graph theory with adaptive consensus mechanisms to synchronize fault detection and isolation while dynamically adjusting operational parameters to preserve system stability and efficiency. Experimental results demonstrate that SAC-FMA outperforms traditional fault management approaches with a 41% improvement in convergence rate, 67% enhancement in fault containment efficiency, 60% reduction in system instability, 95% maintenance of operational performance, and 40% decrease in communication overhead. These findings highlight SAC-FMA’s potential for significantly enhancing resilience and reliability in smart manufacturing environments.