基于GA优化BP神经网络的液压钻机故障诊断
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河南理工大学电气工程与自动化学院焦作454000

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TP183

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Faultdiagnosis of hydraulic drilling rig based on Genetic algorithm optimized BP neural network
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School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000,China

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    摘要:

    针对BP神经网络应用于故障诊断时存在着收敛速度慢、易陷入局部最小值等问题,提出了一种基于遗传算法(GA)优化BP神经网络的液压钻机故障诊断方法。利用GA的选择、交叉和变异操作优化BP神经网络的权值和阈值,提高网络训练的收敛速度。根据液压钻机工况参数提取的特征信号,进行归一化处理建立样本,利用训练样本对网络进行训练,根据训练结果进行故障诊断。仿真结果表明,GA优化的BP神经网络迭代次数少,收敛速度快,能够对测试样本进行有效地分类,故障诊断正确率高。

    Abstract:

    Aiming at the problem that BP neural network have a slow convergence speed and is easy to fall into local minimum when applied to fault diagnosis, a fault diagnosis method of hydraulic drilling rig based on Genetic algorithm optimized BP neural network is proposed. Using selection, crossover and mutation to optimize the weights and thresholds of BP neural network, and improve the convergence speed of network training and the optimized BP neural network is applied in the fault diagnosis of hydraulic drilling rig. Extract appropriate fault feature from the collected parameters of hydraulic drilling rig working condition, then establish sample set by data normalization, using the training sample set to train the network, and finally, make fault diagnosis according to the training results. The simulation reveals that the BP neural network optimized by genetic algorithm has a small number of iterations, and a fast convergence speed. It can classify the testing samples effectively, and it has a high accuracy of fault diagnosis.

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余发山,康洪.基于GA优化BP神经网络的液压钻机故障诊断[J].电子测量技术,2016,39(2):134-137

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  • 在线发布日期: 2016-04-20
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