Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery. In practical deployments, however, raw signal upload is costly and alarm decisions must be made locally under limited computation, changing operating conditions, and strict nuisance-alarm budgets. This paper presents a reliability-calibrated edge-IoT early-warning framework, in which a compact Physics-Guided Tiny-Mamba Transformer (PG-TMT) acts as the representation module and an extreme value theory (EVT) layer converts streaming anomaly scores into event-level alarm episodes. PG-TMT combines a depthwise-separable convolutional stem, a Tiny-Mamba state-space branch, and a lightweight local Transformer to capture transient, long-horizon, and multichannel degradation cues under batch-size-one inference. To improve auditability, temporal attention is projected to the frequency domain and softly aligned with analytical bearing fault-order bands. EVT calibration, dual-threshold hysteresis, and trimmed-tail fitting provide controllable false-alarm intensity even when healthy calibration data are imperfect. Experiments on CWRU, Paderborn, XJTU-SY, and an industrial pilot demonstrate that the proposed framework improves PR-AUC, reduces detection delay under a controlled nuisance-alarm budget, and remains robust to structured interference, metadata uncertainty, compound fault mixtures, and domain transfer. With a sub-1 MB footprint and Jetson p99 latency below 7 ms, the framework supports calibrated and interpretable early warnings for IIoT predictive maintenance.

翻译：工业物联网系统日益依赖分布式振动传感来支撑旋转机械的预测性维护。然而，在实际部署中，原始信号上传成本高昂，且报警决策必须在有限计算资源、变化工况以及严格的误报警预算下本地做出。本文提出了一种可靠性校准的边缘-物联网早期预警框架，其中紧凑型物理引导的微型Mamba Transformer作为表征模块，而极值理论层则将流式异常得分转化为事件级报警片段。PG-TMT结合了深度可分离卷积主干、微型Mamba状态空间分支和轻量级局部Transformer，以在批量大小为1的推理条件下捕获瞬态、长时域及多通道退化线索。为提升可审计性，时间注意力被投影至频域，并与分析型轴承故障阶次带进行软对齐。极值理论校准、双阈值迟滞和修整尾部拟合即使在健康校准数据不完美的情况下也能提供可控的虚警强度。在CWRU、Paderborn、XJTU-SY以及工业试点上的实验表明，所提框架提升了PR-AUC，在可控误报警预算下降低检测延迟，并对结构化干扰、元数据不确定性、复合故障混合及领域迁移保持鲁棒性。该框架拥有低于1 MB的模型体积，且Jetson上的p99延迟低于7毫秒，为IIoT预测性维护提供了校准且可解释的早期预警。