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。 In order to meet the need of on-line fault identification in coal mine drilling, a fault diagnosis expert system model of drilling engineering based on integrated neural network is constructed. The system uses BP network as a sub-neural network, uses the voting method as the output method of the integrated neural network, and verifies the accident through the forward-reverse hybrid reasoning mechanism. Practice has proved that the system can effectively prevent and control the occurrence of drilling accidents on the basis of enhancing the drilling efficiency.