The Comparative Experiments between the Vibration Signal and the Current signal of Rotor System based on Deep Learning Method

Haihong Tang, Peng Chen, Dunwen Zuo, Yi Sheng, Qing-Ping Mei
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Abstract

for comparative experiments between the vibration signal and the current signal, an intelligent fault diagnosis method based on multiclass convolutional neural network (MCNN) has been proposed to investigate the vibration and current signal for identifying those faults in complex rotor system. Firstly, the vibration and current signal, including bearing and structural faults, were recorded simultaneously under steady-state for each operation condition (three kinds of speed). Secondly, the signal processing technique is chosen to solve the problem of modeling noise instances as true underlying relationship for MCNN. Finally, a one-versus-one and a comprehensive MCNN have been trained with both signal at various operating conditions individually and collectively, respectively. And the experimental results revealed that the accuracy of the vibration signal is better than the current signal whether it is structure faults or the external bearing faults. Moreover, the fault diagnosis performance of a one-versus-one or a comprehensive MCNN is investigated for the wide range of MCNN parameters. The experimental results shown that the vibration signal of the bearing with the high-pass filter and envelop has stable accuracy.
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基于深度学习方法的转子系统振动信号与电流信号的对比实验
为了对振动信号和电流信号进行对比实验,提出了一种基于多阶卷积神经网络(MCNN)的复杂转子系统振动和电流信号智能诊断方法。首先,同时记录各工况(三种转速)稳态下的振动和电流信号,包括轴承故障和结构故障;其次,采用信号处理技术,解决了将噪声实例建模为MCNN的真实底层关系的问题。最后,分别在不同的操作条件下单独和集体地训练了一个一对一和一个综合的MCNN。实验结果表明,无论是结构故障还是外轴承故障,该振动信号的精度都优于当前信号。此外,在MCNN参数范围较大的情况下,研究了一对一或综合MCNN的故障诊断性能。实验结果表明,采用高通滤波和包络技术处理的轴承振动信号精度稳定。
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