Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance

TL;DR

SEMAS presents a self-evolving, three-tier multi-agent system for Industrial IoT predictive maintenance that achieves real-time anomaly detection with strong interpretability. By distributing specialized edge, fog, and cloud agents and enabling gradient-based policy evolution via PPO, SEMAS delivers substantial latency reductions and robust adaptation under evolving operational conditions. The framework integrates LLM-based explanations to foster operator trust and employs federated aggregation for collaborative policy sharing without centralized data gathering. Empirical results on Boiler Emulator and Wind Turbine datasets show SEMAS outperforms rule-based adaptive baselines, with statistically robust improvements in complex/imbalanced settings and dramatic improvements in latency, making real-time deployment feasible at scale.

Abstract

Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge deployment. We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers. Edge agents perform lightweight feature extraction and pre-filtering; Fog agents execute diversified ensemble detection with dynamic consensus voting; and Cloud agents continuously optimize system policies via Proximal Policy Optimization (PPO) while maintaining asynchronous, non-blocking inference. The framework incorporates LLM-based response generation for explainability and federated knowledge aggregation for adaptive policy distribution. This architecture enables resource-aware specialization without sacrificing real-time performance or model interpretability. Empirical evaluation on two industrial benchmarks (Boiler Emulator and Wind Turbine) demonstrates that SEMAS achieves superior anomaly detection performance with exceptional stability under adaptation, sustains prediction accuracy across evolving operational contexts, and delivers substantial latency improvements enabling genuine real-time deployment. Ablation studies confirm that PPO-driven policy evolution, consensus voting, and federated aggregation each contribute materially to system effectiveness. These findings indicate that resource-aware, self-evolving 1multi-agent coordination is essential for production-ready industrial IoT predictive maintenance under strict latency and explainability constraints.

Figures (4)

• Figure 1: High-level self-evolving hierarchical MAS architecture for IIoT predictive maintenance.
• Figure 2: Detailed agent-level architecture showing all subagents, communication protocols, and feedback loops.
• Figure 3: Comprehensive performance comparison across all systems and metrics. SEMAS achieves superior F1-scores (Boiler: 0.4873, Wind Turbine: 0.9571), highest ROC-AUC values (0.6118 and 0.7583), and dramatically lower latency (1.22ms and 0.30ms) compared to Baseline1 (1923ms and 456ms) and Baseline2 (1594ms and 286ms). The multi-metric visualization demonstrates SEMAS's dual advantage in both accuracy and computational efficiency.
• Figure 4: F1-score evolution across iterations for Baseline1, Baseline2, and SEMAS on both datasets. SEMAS demonstrates stable convergence and positive learning on challenging data.