摘要
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。
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.
作者
李杰其
胡良兵
LI Jieqi;HU Liangbing(Institute of Plasma Physics Chinese Academy of Sciences,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;University of Science and Technology of China,Hefei 230026,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第21期11-19,共9页
Computer Engineering and Applications
基金
安徽省自然科学基金面上项目(No.1808085ME124)。
关键词
预测性维护
寿命预测
机器学习
人工神经网络
支持向量机
聚类算法
随机森林
predictive maintenance
life prediction
machine learning
artificial neural network
support vector machine
cluster analysis
random forest
