基于机器学习的设备预测性维护方法综述
Review of Machine Learning for Predictive Maintenance
查看参考文献64篇
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文摘
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机器学习算法能够处理高维和多变量数据,并在复杂和动态环境中提取数据中的隐藏关系,在预测性维护技术中具有很好的应用前景。然而,预测性维护系统的性能取决于机器学习算法的选择,对目前应用与预测性维护中的机器学习算法进行综述,详细比较了几种机器学习算法的优缺点,并对机器学习在预测性维护研究中的应用进行了展望。 |
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其他语种文摘
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Machine learning algorithms can process high-dimensional and multi-variable data, and extract hidden relationships in the data in complex and dynamic environments, and have good application prospects in predictive maintenance technology. However, the performance of predictive maintenance system depends on the choice of machine learning algorithms. This paper reviews the current machine learning algorithms used in predictive maintenance system, compares the advantages and disadvantages of several machine learning algorithms characteristic in detail. The application of the machine learning in predictive maintenance is prospected in the future. |
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来源
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计算机工程与应用
,2020,56(21):11-19 【扩展库】
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DOI
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10.3778/j.issn.1002-8331.2006-0016
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关键词
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预测性维护
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寿命预测
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机器学习
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人工神经网络
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支持向量机
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聚类算法
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随机森林
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地址
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1.
中国科学院合肥物质科学研究院等离子体物理研究所, 合肥, 230031
2.
中国科学技术大学, 合肥, 230026
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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1002-8331 |
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学科
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自动化技术、计算机技术 |
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基金
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安徽省自然科学基金面上项目
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文献收藏号
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CSCD:6834785
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64
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