<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSSD</journal-id><journal-title-group><journal-title>Scientific and Social Sustainable Development</journal-title></journal-title-group><issn>3066-8964</issn><eissn>3066-8980</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SSSD.2025040032</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>露天煤矿电铲提升减速机润滑油在线监测与智能故障预警系统研究</title><url>https://artdesignp.com/journal/SSSD/1/4/10.61369/SSSD.2025040032</url><author>景晨,朱宴南,柳海生,杨志佳</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>4</issue><history><date date-type="pub"><published-time>2025-04-28</published-time></date></history><abstract>露天煤矿电铲提升减速机作为关键设备，其可靠性直接影响生产效率和安全性。本文针对传统润滑管理方法的不足，提出了一种基于在线监测与智能故障预警的润滑油管理系统。该系统通过监测提升减速机的金属磨粒、水分含量和粘度等参数，结合机器学习算法，实现对设备磨损状态和故障趋势的预测，为设备维护提供科学依据，提高设备可靠性和降低维护成本。</abstract><keywords>露天煤矿,电铲提升减速机,润滑油在线监测,智能故障预警,机器学习</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 国家能源局.GB/T 35094-2018矿山机械润滑油在线监测技术规范[S]. 北京: 中国标准出版社，2018.[2]Yan X.,et al.Real-time wear debris monitoring in gearboxes using inductive sensors[J].Tribology International,2020,152:106532.[3Johnson K.L.,et al.Wireless sensor networks for industrial equipment monitoring[J].IEEE Sensors Journal,2022,22(6):5812-5820.[4] 朱洪波等. 露天采矿设备油液多参数实时监测系统设计[J]. 工矿自动化,2019,45(3):54-60.[5]Smith J.R.,et al.IoT-based lubrication health management for mining machinery[J].Engineering Failure Analysis,2021,126:105463.[6] 张勇, 刘洋. 基于机器学习的油液磨粒趋势预测模型[J]. 振动与冲击,2022,41(7):76-82.[7]Li H.,et al.Early fault warning of gearboxes by combining vibration and oil debris analysis[J].Mechanical Systems and Signal Processing,2019,130:248-263.[8] 王建华, 李志强. 基于油液分析的齿轮箱磨损状态监测技术[J]. 机械工程学报,2020,56(12):102-110.[9] 杨斌, 张涛. 露天矿大型设备智能故障预警系统研究[J]. 煤炭科学技术,2021,49(5):189-195.[10]Peng Z.,et al.Deep learning-based anomaly detection in oil condition monitoring[J].Reliability Engineering &amp;amp; System Safety,2023,230:108956.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
