基于神经网络的钻探事故类型判别模型研究  

Research on drilling fault diagnosis model of equipment based on neural network

作　　者：蒲春 赵阳刚 杨斌[1] 陈映 PU Chun;ZHAO Yanggang;YANG Bin;CHEN Ying(Civil‑Military Integration Center of China Geological Survey,Chengdu Sichuan 610036,China)

机构地区：[1]中国地质调查局军民融合地质调查中心,四川成都610036

出　　处：《钻探工程》2023年第S01期555-560,共6页Drilling Engineering

基　　金：中国地质调查局军民融合地质调查中心项目“青藏高原寒区资源与环境调查监测与评价”(编号:DD20220881)。

摘　　要：钻探孔内事故会造成严重的损失,若钻探设备能及时判断孔内事故类型,则可缩短事故处理时间,遏制事态发展。提出了一种基于神经网络的钻探事故类型判别模型。为了优选不同神经网络在事故类型判别时的正确率,在Matlab的nntool工具箱中分别构建了BP、RBF两种神经网络模型,将某矿区施工参数变化趋势作为输入参数,通过仿真试验发现,BP神经网络中表现最好的是LM、BR算法,RBF神经网络中表现最好的是PNN算法,三者准确率均可在90%以上,但BP神经网络容易陷入局部最优,性能不稳定,偶有判别错误的现象,而PNN神经网络无此局限,且不需要训练。通过对比,PNN算法更适用于事故类型判别模型建立。Drilling accidents can cause serious economic losses,wasted time,and even threaten life safety.If the drilling equipment can judge the type of accident in time,the accident processing time can be shortened and the development of the situation can be contained.To solve the above problems,this paper proposes a drilling fault diagnosis model of equipment based on neural network.In order to optimize the correct rate of different neural networks in drilling accidents classification,two neural network models of BP and RBF are constructed respectively in nntool of Matlab.Through the simulation test taking the variation trend of construction parameters in a mining area as input parameters,it is found that the best performance in BP neural network is LM and BR algorithm,and the best performance of RBF neural network is PNN algorithm.All three had an accuracy rate of more than 90 percent.But BP neural network is easy to fall into local optimal with unstable performance.On the contrary,PNN neural network has no such limitation,does not require training,and the design process is simple.So PNN algorithm is more suitable for the establishment of drilling fault diagnosis model.

关 键 词：钻探设备 孔内事故 类型判别 MATLAB BP神经网络 RBF神经网络 PNN算法 

分 类 号：P634.8[天文地球—地质矿产勘探]