摘要
石油钻杆的使用情况关系到钻采设备的安全运行。对石油钻杆在线检测,可以节省费用,保障钻采安全。设计了基于LM神经网络的钻杆在线漏磁缺陷识别系统,该系统在钻杆从井口的提升过程中进行缺陷检测,将采集到的数据存到计算机中进行处理,对截取的信号片段利用Levenberg-Marquardt神经网络进行训练,达到对缺陷模式进行定性分类和定量识别。
Drill and casing pipes are one of the necessary drilling equipment in the oilfields. The conditions of the pipes lead to the safety of the drilling equipment. It is important to monitor the drill and casing pipes on time so as to ensure their safety. The on-line inspection system was designed in this paper. When the drill and easing pipes were raised from wellhead, the data were stored in central computer as the processing. The signal fragments were trained by Levenberg-Marquardt neural network so as to deal with the qualitative classification and quantitative identification.
出处
《装备制造技术》
2009年第2期87-89,共3页
Equipment Manufacturing Technology
关键词
钻杆
漏磁检测
神经网络
drill and casing pipes
magnetic flux leakage inspection
neural networks
