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.

翻译：工业物联网预测性维护需要系统能够在保持可解释性且不消耗过量计算资源的前提下实现实时异常检测。传统方法依赖于静态的离线训练模型，无法适应持续演变的运行工况；而基于大语言模型的单体系统则对内存和延迟要求过高，难以在实际边缘场景中部署。本文提出SEMAS，一种自演进的分层多智能体系统，将专用智能体分布在边缘、雾和云计算层级。边缘智能体执行轻量级特征提取与预过滤；雾智能体通过动态共识投票执行多样化集成检测；云智能体通过近端策略优化持续优化系统策略，同时维持异步非阻塞推理。该框架融合了基于大语言模型的响应生成机制以提供可解释性，并采用联邦知识聚合实现自适应策略分发。此架构在保障实时性能与模型可解释性的同时，实现了资源感知的专用化设计。在两个工业基准数据集（锅炉仿真器与风力涡轮机）上的实证评估表明，SEMAS在适应过程中展现出卓越的稳定性与优异的异常检测性能，能够在持续演变的运行环境中保持预测精度，并通过显著的延迟优化实现真正的实时部署。消融研究证实，近端策略优化驱动的策略演进、共识投票机制及联邦聚合均对系统效能具有实质性贡献。这些发现表明，在严格的延迟与可解释性约束下，资源感知的自演进多智能体协同机制是实现生产级工业物联网预测性维护的关键。