基于TinyML的分层推理框架，实现矿用机械实时故障预测。
Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled Hierarchical Inference Network
- 分层部署模型，根据资源动态选择在设备、网关或云端推理。
- 传感器端推理准确率超90%，功耗降低44%，续航达104小时。
- 低延迟（3.33毫秒）适合边缘场景，适合远程工业环境应用。
采矿机械在多变环境中运行，面临高磨损和不可预测应力，给预测性维护（PdM）带来挑战。本文提出面向预测性维护的边缘传感网络（ESN-PdM），一种跨边缘设备、网关和云服务的分层推理框架，用于实时状态监测。系统根据准确率、延迟和电池寿命的权衡，动态调整推理位置——在设备、网关或云端进行，利用TinyML技术优化资源受限设备上的模型。性能评估显示，传感器端和网关端推理的分类准确率均超过90%，云端推理达到99%。传感器端推理使功耗降低约44%，支持长达104小时运行。设备端推理延迟最低（3.33毫秒），向网关（146.67毫秒）或云（641.71毫秒）卸载时延迟上升。ESN-PdM框架为可靠异常检测与维护提供了可扩展、自适应的解决方案，对保障远程环境下设备持续运行至关重要。通过平衡准确率、延迟与能耗，该方法推动了工业应用场景中预测性维护体系的发展。
原文摘要 · Abstract (English)
Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), a hierarchical inference framework across edge devices, gateways, and cloud services for real-time condition monitoring. The system dynamically adjusts inference locations--on-device, on-gateway, or on-cloud--based on trade-offs among accuracy, latency, and battery life, leveraging Tiny Machine Learning (TinyML) techniques for model optimization on resource-constrained devices. Performance evaluations showed that on-sensor and on-gateway inference modes achieved over 90\% classification accuracy, while cloud-based inference reached 99\%. On-sensor inference reduced power consumption by approximately 44\%, enabling up to 104 hours of operation. Latency was lowest for on-device inference (3.33 ms), increasing when offloading to the gateway (146.67 ms) or cloud (641.71 ms). The ESN-PdM framework provides a scalable, adaptive solution for reliable anomaly detection and PdM, crucial for maintaining machinery uptime in remote environments. By balancing accuracy, latency, and energy consumption, this approach advances PdM frameworks for industrial applications.
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