摘要
设备的健康状态对于现代工业的生产安全和生产效率有着重要的影响,为了准确感知设备的健康状态,实现设备状态从定性分析到定量分析的过渡,提出了一种带噪声基于密度的聚类算法(DBSCAN),通过与高斯混合模型(GMM)进行结合,前者实现数据的分类,后者实现数据的建模,建立了基于改进DBSCAN-GMM的设备健康状态数据量化模型,并结合现场棒磨设备历史运行数据进行了实例分析,验证了该模型的有效性。
The health status of equipments has an important impact on the production safety and production efficiency of modern industry.In order to accurately perceive the health status of equipment and realize the transition from qualitative analysis to quantitative analysis of equipment status,a density-based spatial clustering of applications with noise(DBSCAN)is proposed and combined with Gaussian mixture model(GMM),the former realizes the classification of data,and the latter realizes the modeling of data.A quantitative model of equipment health status data based on improved DBSCAN-GMM is established,and an example analysis is carried out with the historical operation data of rod mill equipment,which verifies the effectiveness of the model.
作者
曾宪利
徐华志
李波波
ZENG Xianli;XU Huazhi;LI Bobo(Jiangxi Duchang Jinding Tungsten Molybdenum Mining Co.,Ltd.;School of Mechanical&Electrical Engineering,Jiangxi University of Science&Technology)
出处
《现代矿业》
CAS
2024年第11期203-207,共5页
Modern Mining
作者简介
曾宪利(1988-),男,副经理,工程师,332606江西省九江市。
