Abstract: To address the problems of high failure rates, reliance on manual experience for diagnosis, and long downtime in traditional lubrication systems for main bearings of mining mills, this study conducted a system failure mechanism analysis, constructed a lubrication system expert knowledge base including a rule base and a case base, and developed a fault diagnosis expert system based on a combination of CBR (case-based reasoning) and RBR (rule-based reasoning). This formed the core technical support for an intelligent lubrication system for mill main bearings. The system monitors lubrication system operating parameters in real time through sensors, enabling fault early warning, rapid matching, and in-depth diagnosis, and outputs targeted solutions. Simultaneously, it wirelessly transmits equipment status information to a cloud-based mining equipment industrial internet platform, supporting intelligent services such as remote monitoring, fault diagnosis, and preventive maintenance. Engineering applications show that this system effectively improves the operational reliability and intelligence level of the mill main bearing lubrication system, shortens downtime, reduces maintenance costs, and provides technical support for the intelligent and unmanned development of mining equipment.
李亚航, 万妍, 赵雅帆. 矿用磨机主轴承智能润滑系统及故障诊断专家系统研究[J]. 水泥技术, 2026, 1(3): 82-88.
LI Yahang, WAN Yan, ZHAO Yafan. Research on Intelligent Lubrication System and Fault Diagnosis Expert System for Main Bearing of Mining Mill. Cement Technology, 2026, 1(3): 82-88.
