Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

Yang, Yang; Zeng, Qingliang; Wan, Lirong; Yin, Guangjun

doi:10.1109/access.2020.2997987

Cited by 10 publications

(1 citation statement)

References 57 publications

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“…Liu et al [14] employed the Hilbert-Huang transform on vibration signals to obtain their time-frequency distribution characteristics and then extracted the information entropy to be used as CGR features. Yang et al [15] conducted a numerical analysis of coal and gangue particles with different mixing ratios impacting on the metal plate and then calculated the time-frequency domain parameters of vibration signals. Finally, they obtained the effects of different mixing ratios on the time-frequency domain parameters.…”

Section: Introductionmentioning

confidence: 99%

Coal gangue recognition using multichannel auditory spectrogram of hydraulic support sound in convolutional neural network

Chen¹,

Wang²,

Liu³

et al. 2021

Meas. Sci. Technol.

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Many data-driven coal gangue recognition (CGR) methods based on the vibration or sound of collapsed coal and gangue have been proposed to achieve automatic CGR, which is important for realizing intelligent top-coal caving. However, the strong background noise and complex environment in underground coal mines render this task challenging in practical applications. Inspired by the fact that workers distinguish coal and gangue from underground noise by listening to the hydraulic support sound, we propose an auditory model based CGR method that simulates human auditory recognition by combining an auditory spectrogram with a convolutional neural network (CNN). First, we adjust the characteristic frequency (CF) distribution of the auditory peripheral model (APM) based on the spectral characteristics of collapsed sound signals from coal and gangue and then process the sound signals using the adjusted APM to obtain inferior colliculus auditory signals with multiple CFs. Subsequently, the auditory signals of all CFs are converted into gray images separately and then concatenated into a multichannel auditory spectrum (MCAS) along the channel dimension. Finally, we input the MCAS as a feature map to the two-dimensional CNN, whose convolutional layers are used to automatically extract features, and the fully connected layer and softmax layer are used to flatten features and predict the recognition result, respectively. The CNN is optimized for the CGR based on a comparison study of four typical types of CNN structures with different network training hyperparameters. The experimental results show that this method affords an accurate CGR with a recognition accuracy of 99.5%. Moreover, this method offers excellent noise immunity compared with typically used CGR methods under various noisy conditions.

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Section: Introductionmentioning

confidence: 99%