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利用神经网络的非线性映射及其高度的自组织和自学习能力,将SOM网络应用于钻机液压系统的故障诊断。根据钻机液压系统故障的特点,选取能够表征全液压钻机故障特点的样本,设计相应的SOM神经网络,并在MATLAB环境下实现了对网络的训练和仿真实验,表明该方法有很强的实用性,为全液压钻机的故障诊断提供了一种途径。
Using the nonlinear mapping of neural network and its high ability of self-organization and self-learning, the SOM network is applied to the fault diagnosis of drilling rig hydraulic system. According to the characteristics of hydraulic system failure of drilling rig, this paper selects the samples that can characterize the fault characteristics of full hydraulic drilling rig, designs the corresponding SOM neural network, and realizes the training and simulation experiments on the network under the MATLAB environment, which shows that the method has strong practicability , Provides a way for fault diagnosis of full hydraulic drilling rigs.