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中国科技核心期刊

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期刊导读

人工智能在矿山设备预测性维护中的应用研究

  • 作者:
  • 李涛|孙琰|侯建硕|周斌
  • 作者单位:
  • 万宝矿产有限公司|万宝矿产有限公司|万宝矿产有限公司|万宝矿产有限公司
  • 基金项目:

  • 国家自然科学基金重点项目(52130404)
  • 详细信息:

  • 作者简介:
  • 李涛(1989—),男,高级工程师,博士,研究方向为采矿工程;E-mail:1016990045@qq.com
  • 通讯作者:
  • isnull
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Application of artificial intelligence in predictive maintenance of mining equipment

  • English Author:
  • Wanbao Mining Co.,Ltd.|Wanbao Mining Co.,Ltd.|Wanbao Mining Co.,Ltd.|Wanbao Mining Co.,Ltd.
  • Unit:
  • 摘要
  • 在线预览
  • 参考文献

摘要:

预测性维护(PdM)利用数据和分析来预测系统组件的潜在故障,提前采取维护措施以避免损坏,旨在解决矿山设备维护中的预测性问题,提高设备可靠性和生产效率。研究流程包括数据收集、数据预处理、模型训练与预测、决策支持与执行等环节。从数据源、模型透明性与可解释性、系统集成3个方面分析了利用人工智能实现PdM的挑战。研究结果表明,基于人工智能的PdM能够显著减少设备故障时间,提高维护效率,降低运营成本。此外,提出机器学习、物联网、云计算和数字孪生等技术在PdM中的应用前景,为未来研究提供了方向。

关键词:

预测性维护;人工智能;深度学习;机器学习;数字孪生;区块链技术

Abstract:

Predictive maintenance (PdM) leverages data and analytics to anticipate potential failures of system components,enabling preemptive maintenance measures to prevent damage.This approach aims to address predictive challenges in mining equipment maintenance,enhancing equipment reliability and production efficiency.The research process encompasses stages such as data collection,preprocessing,model training,and prediction,as well as decision support and execution.Challenges in implementing PdM with artificial intelligence are analyzed from 3 perspectives:data sources,model transparency and interpretability,and system integration.The findings indicate that AI-based PdM significantly reduces equipment downtime,improves maintenance efficiency,and lowers operating costs.Additionally,the study outlines the application prospects of technologies such as machine learning,IoT,cloud computing,and digital twins in PdM,offering directions for future research.

Keywords:

predictive maintenance;artificial intelligence;deep learning;machine learning;digital twin;blockchain technology