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针对传统煤矿机械齿轮故障诊断中非线性特征频率导致的信号分析处理要求高等问题,提出了一种基于人工神经网络的煤矿机械齿轮故障诊断方法。首先介绍了神经网络的基本原理及其建模方法。其次将机械齿轮故障敏感参数作为神经网络的输入信号,并参与算法训练,实现机械齿轮故障的不同分类。最后,通过仿真验证了所提方法的可行性和有效性。
Aimed at the high requirement of signal analysis and processing caused by non-linear characteristic frequency in the traditional coal mine machinery gear fault diagnosis, a method of gear fault diagnosis based on artificial neural network is proposed. First introduced the basic principles of neural networks and modeling methods. Second, the sensitive parameters of mechanical gear failure are taken as the input signal of neural network, and participate in the algorithm training to realize the different classification of mechanical gear failure. Finally, the feasibility and effectiveness of the proposed method are verified by simulation.