摘要
针对煤矿钻井在线故障识别的需求,构造一种基于集成神经网络的钻井工程故障诊断专家系统模型。系统利用BP网络作为子神经网络,采用投票法作为集成神经网络的结果输出方法,通过正反向混合推理机制来检验事故。实践证明,该系统在增强钻探效率的基础上,能够有效地预防和控制钻井事故的发生。
This paper put forward a fault diagnosis expert system model of drilling engineering based on integrated neural networks aiming at the demand of on-line fault identification in the coal mine drilling field. The system used BP network as sub-neural network, and adopted the vote method as the result output of integrated neural networks, and verified faults through forward and reverse mixed reasoning mechanism. The experiment proves that this system can enhance drilling efficiency and effectively prevent and control the happening of drilling faults.
出处
《煤矿机械》
北大核心
2014年第2期219-220,共2页
Coal Mine Machinery
基金
黑龙江省教育厅科学技术研究项目(12521474)
关键词
集成神经网络
煤矿钻井
专家系统
故障诊断
integrated neural networks
coal mine drilling
expert system
fault diagnosis
