用物联网和机器学习预测自动售货机故障，减少停机时间。
Predictive Maintenance Optimization for Smart Vending Machines Using IoT and Machine Learning
- 通过物联网传感器实时采集数据，用机器学习预测故障
- 提前发现故障，减少不必要的维修次数
- 适合智能运维、零售设备管理的从业者参考
自动售货机在公共场所和商业环境中的普及，对运营效率和客户满意度提出了更高要求。传统维护方式（如事后维修或定时保养）难以提前预判故障，导致意外停机和运维成本上升。本研究提出一种面向自动售货机的新型预测性维护框架，结合物联网（IoT）传感器与机器学习（ML）算法，实现对设备部件和运行状态的实时监测，并利用预测模型在故障发生前进行预警，从而实现精准维护调度，降低停机时间并延长设备寿命。该框架通过模拟故障数据和分类算法性能评估进行了验证，结果显示早期故障检测能力显著提升，冗余服务干预大幅减少。研究表明，将预测性维护系统集成至售货机基础设施中，可有效提升运营效率与服务可靠性。
原文摘要 · Abstract (English)
The increasing proliferation of vending machines in public and commercial environments has placed a growing emphasis on operational efficiency and customer satisfaction. Traditional maintenance approaches either reactive or time-based preventive are limited in their ability to preempt machine failures, leading to unplanned downtimes and elevated service costs. This research presents a novel predictive maintenance framework tailored for vending machines by leveraging Internet of Things (IoT) sensors and machine learning (ML) algorithms. The proposed system continuously monitors machine components and operating conditions in real time and applies predictive models to forecast failures before they occur. This enables timely maintenance scheduling, minimizing downtime and extending machine lifespan. The framework was validated through simulated fault data and performance evaluation using classification algorithms. Results show a significant improvement in early fault detection and a reduction in redundant service interventions. The findings indicate that predictive maintenance systems, when integrated into vending infrastructure, can transform operational efficiency and service reliability.
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