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

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (11) : 258-264.

CHENG Zhihai1， LIU Huiquan1， LIU Yong2， QIN Huan1， LIU Hailong1

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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.

Key words

coal particle size / acoustic emission signal / BP neural network / recognition

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CHENG Zhihai1， LIU Huiquan1， LIU Yong2， QIN Huan1， LIU Hailong1. Coal particle size recognition based on acoustic emission signal and BP neural network[J]. Journal of Vibration and Shock, 2020, 39(11): 258-264

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