基于模糊神经网络的钻机安全监控系统研究

由于地层的多样性,钻机系统状态、功能和结构的复杂性、非线性及不确定性,借助单一信息源提供的信息,采用常规或传统的故障诊断理论和方法,根据几个主要的故障特征量做出判断,难以实现钻机故障诊断。为此,提出基于信息融合的模糊推理和神经网络相结合的故障综合诊断方法,采用多个异类传感器与现场总线构成安全监控系统,建立故障诊断结构模型,进行模糊神经网络算法的信息融合和学习算法与网络参数确定。试验表明,该系统应用于钻机故障的实时检测和神经网络智能诊断具有良好的效果,对提高钻井过程安全和自动化水平具有重要意义。

Study of drilling-rig safety monitoring system based on fuzzy neural network

Because of the diversity of the stratum and complexity of the oil rig and poor working conditions, dilling-rig fault diagnosis and safety monitoring are the important parts of achieving a fully automated drilling. For oil drilling-rig fault diagnosis and safety monitoring, it makes the comprehensive fault diagnosis based on data fusion of fuzzy reasoning and neural networks. Multiple heterogeneous sensors and field bus constitute the safe monitoring system, then proceeding data fusion of algorithm for fuzzy neural networks and determining the networks parameters. The experiment shows that the system applies to the drilling-rig real-time fault detection and neural network intelligent diagnosis has a good effect, it has the great significance for improving the process of security drilling and automation level.