Coal particle size recognition based on acoustic emission signal and BP neural network
CHENG Zhihai1， LIU Huiquan1， LIU Yong2， QIN Huan1， LIU Hailong1
1.College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200082, China;
2.College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200082, China
Abstract：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 is 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.
程智海1，刘汇泉1，刘勇2，秦欢1，刘海龙1. 基于声发射信号与BP神经网络的煤粉粒径识别研究[J]. 振动与冲击, 2020, 39(11): 258-264.
CHENG Zhihai1， LIU Huiquan1， LIU Yong2， QIN Huan1， LIU Hailong1. Coal particle size recognition based on acoustic emission signal and BP neural network. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 258-264.
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