Coal particle size recognition based on acoustic emission signal and BP neural network

被引：0
|
作者：
Cheng Z. [1 ]
Liu H. [1 ]
Liu Y. [2 ]
Qin H. [1 ]
Liu H. [1 ]
机构：
[1] College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai
[2] College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 11期
关键词：
Acoustic emission signal; BP neural network; Coal particle size; Recognition;
D O I：
10.13465/j.cnki.jvs.2020.11.034
中图分类号：
学科分类号：
摘要：
The measurement of coal particle size is an important task for coal-fired power stations. Aiming at the shortcomings of the current sieving method, a method combining on-line recognition of coal particle size with Acoustic Emission (AE) signal and BP neural network was proposed. The characteristics of the background noise and AE signals were compared in the frequency domain, and the frequency interval related to the particle size was confirmed in the signal. The wavelet packet zeroing method was used to de-noise the AE signal, and the de-noising performance of different wavelet function was compared in terms of signal-to-noise ratio and signal smoothness. Through the power spectrum analysis, the characteristics of signal energy with the particle size were found. Finally, the signal energy characteristics were extracted, and BP neural network was used to recognize the particle size. The research indicates that the acoustic emission technology and BP neural network can be used to monitor the coal particle size. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：258 / 264
页数：6
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