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Research on Gear-box Fault Diagnosis Method Based on Adjusting-learning-rate PSO Neural Network

机译:基于学习率PSO神经网络的齿轮箱故障诊断方法研究

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摘要

Based on the research of Particle Swarm Optimization (PSO) learning rate, two learning rates are changed linearly with velocity-formula evolving in order to adjust the proportion of social part and cognitional part; then the methods are applied to BP neural network training, the convergence rate is heavily accelerated and locally optional solution is avoided. According to actual data of two levels compound-box in vibration lab, signals are analyzed and their characteristic values are abstracted. By applying the trained BP neural networks to compound-box fault diagnosis, it is indicated that the methods are sound effective.
机译:基于粒子群优化(PSO)学习率的研究,随着速度公式的发展,两个学习率线性变化,以调节社会部分和认知部分的比例。然后将该方法应用于BP神经网络训练,大大加快了收敛速度,避免了局部求解。根据振动实验室中二级复合箱的实际数据,对信号进行分析,提取其特征值。通过将训练后的BP神经网络应用于复合箱故障诊断,表明该方法是行之有效的。

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