基于灰色神经网络的旋转控制头深沟球轴承温度预测  

TEMPERATURE PREDICTION ON DEEP GROOVE BALL BEARING OF ROTARY CONTROL HEAD BASED ON GREY NEURAL NETWORK

作　　者：王国荣[1] 张敏[1] 胡刚[1] 夏焱 

机构地区：[1]西南石油大学机电工程学院,成都610500 [2]中国石油集团钻井工程技术研究院,北京100195

出　　处：《机械强度》2016年第1期12-16,共5页Journal of Mechanical Strength

基　　金："十二五"国家油气重大专项资助(2011ZX05036-003)资助~~

摘　　要：由于井口、转盘、天车的中心偏差,旋转控制头深沟球轴承受到较大的侧向载荷,同时,由于摩擦扭矩的作用,深沟球轴承会产生大量的热,容易导致轴承温度过高而失效,降低旋转控制头现场生产安全性能。为了优选、及时更换润滑冷却介质,提高深沟球轴承使用寿命及旋转控制头整体工作性能,将深沟球轴承温度变化作为研究重点,以室内试验测得数据为样本,运用灰色神经网络方法,建立数学模型对深沟球轴承温度进行预测,并与BP神经网络预测结果作对比。结果表明:灰色神经网络模型预测精度高、稳定性好,且所需样本数据少,对深沟球轴承温度预测及旋转控制头冷却润滑系统设计具有重要的应用价值。Due to the center deviation among wellhead, rotary table and crown block, the deep groove ball bearing of rotary control head （RCH） is affected by large lateral load. In the meantime, deep groove ball bearing can produce a large amount of heat because of the effect of friction torque, which is easy to cause the failure of bearing on account of high temperature and reduce production safety performance on site of RCH. In order to optimize and timely replace the lubrication cooling medium, furthermore, to improve the service life of the deep groove ball bearing and the overall performance of RCH, this paper pays attention to the temperature change of deep groove ball bearing. Based on the laboratory test datas, grey neural network was applied to establish mathematic model for predicting the temperature of deep groove ball bearing, and compared with BP neural network. The results show that grey neural network has the advantages of high prediction precision, good stability and less sample data, which has important application value on the temperature prediction of deep groove ball bearing and the design of RCH＇ s cooling lubrication system.

关 键 词：旋转控制头 深沟球轴承 灰色神经网络 温度预测 

分 类 号：X937[环境科学与工程—安全科学] TE931[石油与天然气工程—石油机械设备]