Establishment of Hierarchical Health Degree Model and Management System for Mechanical Equipment
The large-scale mechanical high-end equipment is an important guarantee for the rapid development of the so-cial economy and national security.To address the quantification issue of the health degree for parts,components,and equip-ment,a quantitative model for the health degree was proposed by combining parts feature parameters and cluster equipment big data,using Long Short-Term Memory network(LSTM)and cross-correlation coeffiicient mean method.The health degree was optimized by fusing fuzzy C-means clustering and Euclidean norm(L2 norm)method.Based on the weight of each part,the health degree transfer model is proposed to realize the quantification of health degrees for components and equipment.In re-sponse to the implementation issues in databases and health management systems,taking the example of mine hoists,a hierar-chical closed-loop system based on the industrial internet is established,which can achieve the integration of data acquisition,edge collaboration,status monitoring,fault diagnosis,quantitative evaluation,and maintenance decision.Through the demonstra-tion application in Luoyang CITIC Heavy Industries,a foundation has been laid for the healthy,stable,and intelligent operation of large-scale,high-end mechanical equipment.
health degree modelmine hoistdatabasehealth management system
万方数据
维普
