摘要
齿轮箱作为风电机组故障率最高的部件,结构复杂、运维困难,影响整机故障率、可靠性、运维成本及发电效率等。通过对风电齿轮箱故障诊断的主要理论、方法及研究现状进行分析并总结存在的问题,提出"五性三化"即动态性、阶段性、多维性、适用性、集成性、定量化、标准化以及统一化共8个方面的解决思路,并结合网络技术、信息技术、大数据技术及人工智能的优势,预测云监测、智能诊断及主动报警的发展趋势。
As the component with the highest failure rate of wind turbine,gear box is complicated and difficult to operate and maintain,which affect the failure rate,reliability,maintenance cost of the whole machine and power generation efficiency of wind field. The main theories,methods and research status of fault diagnosis of wind turbine gearbox were analyzed and summarized,and the eight solutions to the existing problems were put forward,namely dynamic character,stage character,multidimensional character,applicable character,integrated character,quantitative character,standardized character and unification character.The development trend of cloud monitoring,intelligent diagnosis and active warning was proposed by combining the advantages of network technology,information technology,big data technology and artificial intelligence.
作者
徐启圣
白琨
徐厚昌
张春鹏
XU Qisheng;BAI Kun;XU Houchang;ZHANG Chunpeng(Department of Mechanical Engineering,Hefei University,Hefei Anhui 230061,China)
出处
《润滑与密封》
CAS
CSCD
北大核心
2019年第8期138-147,共10页
Lubrication Engineering
基金
安徽省高校优秀青年人才支持计划重点项目(gxyqZD2016276)
关键词
风电发电机
齿轮箱
监测技术
大数据
云监测
智能诊断
wind turbines
gearbox
monitoring technology
big data
cloud monitoring
intelligent diagnosis
