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在智能端口使用物联网传感器数据进行基于机器学习的预测性维护.

Sheraz Aslam1, Alejandro Navarro2, Andreas Aristotelous1

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概括
此摘要是机器生成的。

本研究引入了一种机器学习方法,用于预测海港集装箱处理设备 (CHE) 的故障. 人工神经网络实现了98.7%的准确性,提高了港口运营效率和可靠性.

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科学领域:

  • 海上物流的海上物流
  • 机械工程 机械工程
  • 数据科学数据科学数据科学

背景情况:

  • 海港是全球集装箱货物物流的重要节点,依赖于高效的集装箱处理设备 (CHE).
  • 低效的CHE维护导致运营中断,供应链延迟和等待时间增加.
  • 智能维护策略对于优化港口运营和资源利用至关重要.

研究的目的:

  • 开发一种基于机器学习 (ML) 的方法来预测CHE的故障.
  • 提高港口设备的可靠性和提高整体港口性能.
  • 解决因风扇故障和过器堵塞等问题引起的逆变器过高温度故障.

主要方法:

  • 开发了一个统计模型来评估液压系统的健康状况.
  • 训练和评估了几种ML模型,包括人工神经网络 (ANNs),决策树 (DTs),随机森林 (RF),极端梯度增强 (XGBoost) 和高斯天真贝斯 (GNB).
  • 这些模型被用来预测CHE的逆变器过高温度故障.

主要成果:

  • 人工神经网络 (ANN) 在故障预测方面表现最高.
  • 在预测特定的CHE故障方面,ANN实现了98.7%的准确性和98.0%的F1得分.
  • 开发的ML模型有效地识别了潜在的设备故障.

结论:

  • 基于ML的方法显著改善了CHE故障的预测,提高了设备的可靠性.
  • 在港口实施智能维护策略可以优化运营效率和资源管理.
  • 该研究强调了ANN在海上物流中的潜力,用于预测性维护.