炼化大机组设备预测性维护模型的研究与实践  

Research and Practice on Predictive Maintenance Model for Large Refinery Unit Equipment

作　　者：柳明军 石秀芳 李勇 周国强 刘妤 LIU Ming-jun;SHI Xiu-fang;LI Yong;ZHOU Guo-qiang;LIU Yu(Shandong Institute of information Technology Industry Development,Jinan 250014,China;Shandong Jingbo Holding Group Co.,Ltd,Binzhou 256500,China;Shandong Saibao electronic information product supervision and Testing Research Institute,Ji'nan 250014,China)

机构地区：[1]山东省信息技术产业发展研究院,山东济南250014 [2]山东京博控股集团有限公司,山东滨州256500 [3]山东赛宝电子信息产品监督检测研究院,山东济南250014

出　　处：《山东工业技术》2024年第1期69-73,共5页Journal of Shandong Industrial Technology

摘　　要：石油炼化工艺流程的高温高压、易燃易爆、有毒有害、连续生产的特点对炼化装置的运行要求非常苛刻,只有长周期、满负荷、不间断的安全运行才能保证生产的安全性、产能的连续性以及经济效益的最大化。当前基于大数据的设备智能化预测性维护已经成为石油炼化行业趋势和共识。本文探讨了一种通过采集工业现场大机组设备运行数据,采用多种分析手段建立预测性模型,对炼化大机组设备运行进行综合实时状态评估、故障部位精准定位以及设备维护提前预警,从而实现大机组设备预测性维护。The characteristics of high temperature and high pressure,flammable and explosive,toxic and harmful,and continuous production in the petroleum refining process have very strict requirements for the operation of refining equipment.Only long-term,full load,and uninterrupted safe operation can ensure the safety of production,the continuity of production capacity,and the maximization of economic benefits.Currently,intelligent predictive maintenance of equipment based on big data has become a trend and consensus in the petroleum refining industry.This article explores a method of collecting operational data of large unit equipment in industrial sites,using various analytical methods to establish predictive models,and conducting comprehensive real-time status evaluation,precise positioning of fault locations,and early warning of equipment maintenance for the operation of refining and chemical large unit equipment,in order to achieve predictive maintenance of large unit equipment.

关 键 词：炼化大机组 大数据 人工智能 预测性维护 

分 类 号：U284.71[交通运输工程—交通信息工程及控制]