Study on wavelet packet decomposition method for fault signal of shearer cutting unit transmission system

被引：0
作者：
Liu X. [1 ]
Zhao L. [1 ]
Fu D. [1 ]
Zhang F. [1 ]
机构：
[1] College of Mechanical Engineering, Liaoning Technical University, Fuxin
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2019年 / 38卷 / 14期
关键词：
Cutting unit; Fault diagnosis; Neural network; Shearer; Wavelet packet decomposition method;
D O I：
10.13465/j.cnki.jvs.2019.14.024
中图分类号：
学科分类号：
摘要：
The shearer cut coal and rock mainly through the drum on the cutting unit. If the transmission system fails, the mining work will be interrupted, resulting in huge economic losses. Based on MG2×70/325 shearer, the common fault models of broken teeth, cracked gears and defective bearings were established by PRO/E, and the rigid-flexible coupling virtual prototype model of shearer cutting unit was established by using ANSYS and ADAMS. The axial and radial force data of each idler shaft were extracted as samples to establish the fault diagnosis system. Coif4 wavelet was used to decompose the data, and the energy value of each sub-band was obtained as the input vector of neural network. Combined with Elman neural network, the fault diagnosis model of shearer cutting coal and rock was established. The simulation results showed that this method can effectively diagnose the fault parts and types of transmission system, and has certain guiding significance for shearer fault detection and on-line real-time monitoring under complex working conditions. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：169 / 175and253
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