摘要
针对经典的谱聚类算法运算效率较低,鲁棒性较差等缺点,设计一种新型的谱聚类算法,引入数据场模型,用于剔除孤立数据以及初始聚类中心点的确定,最后通过UCI数据集和提升机轴承故障数据验证了算法的优越性,能够有效提升矿井提升机故障检测系统性能。
Aiming at the shortage of low calculating efficiency and poor robustness of classical spectral clustering algorithm,a new spectral clustering algorithm has been designed and introduced in data field mode to eliminate isolated data and determine initial cluster centers,finally the calculating advantage has been tested and verified by UCI dataset and fault data of hoist bearings,which can effectively improve the performance of fault detection system of mine hoister.
出处
《江西煤炭科技》
2017年第4期83-85,89,共4页
Jiangxi Coal Science & Technology
关键词
矿井提升机
故障诊断
谱聚类
数据场
mine hoister
fault diagnosis
spectral clustering
data field