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.

翻译：在多变环境中运行的采矿机械面临高磨损和不可预测的应力，这对预测性维护提出了挑战。本文介绍了用于预测性维护的边缘传感器网络，这是一种跨越边缘设备、网关和云服务的分层推理框架，用于实时状态监测。该系统基于精度、延迟和电池寿命之间的权衡，动态调整推理位置——在设备端、网关端或云端——并利用微型机器学习技术在资源受限的设备上进行模型优化。性能评估表明，传感器端和网关端推理模式的分类准确率超过90%，而基于云的推理达到99%。传感器端推理降低了约44%的功耗，可实现长达104小时的运行。设备端推理的延迟最低（3.33毫秒），卸载到网关（146.67毫秒）或云端（641.71毫秒）时延迟增加。该框架为可靠的异常检测和预测性维护提供了一个可扩展、自适应的解决方案，对于在偏远环境中维持机械正常运行时间至关重要。通过平衡精度、延迟和能耗，该方法推动了工业应用的预测性维护框架的发展。