Research on coal-gangue identification technology driven by multi-source fusion of image features and vibration spectrum
LI Libao
YUAN Yong
QIN Zhenghan
LI Bo
YAN Zhengtian
LI Yong
Abstract：To address the challenges of feature fusion,real-time performance,and model complexity in the application of image and vibration signal fusion for coal-gangue identification,a multi-head attention(MA)-based multi-layer long short-term memory(ML-LSTM)model,i.e.,MA-ML-LSTM,was proposed.The variational mode decomposition(VMD)algorithm,optimized by particle swarm optimization(PSO),was employed to process vibration signals.Features such as energy,energy moment,kurtosis,waveform factor,and matrix singular values were extracted.A one-dimensional convolutional network was used to acquire vibration information.For image feature extraction,the fully connected layer of the multi-classification network ResNet-18 was removed,enabling the extraction of deep features from coal-gangue images.Dual-channel feature fusion of images and vibration signals was achieved using the MA mechanism and the ML-LSTM network,enhancing the expression of significant features in each channel.Experimental results demonstrated that the MA-ML-LSTM model achieved an average recognition accuracy of 98.72%,which was 4.60%,7.96%,5.37%,and 6.11%higher than traditional single models ResNet,MobilenetV3,1D-CNN,and LSTM,respectively.Compared to EMD-RF,IMF-SVM,and CSPNet-YOLOv7 models,accuracy improved by 4.18%,4.45%,and 3.46%,respectively.These findings validate the effectiveness of the coal-gangue identification technology driven by multi-source fusion of image features and vibration spectrum.
Keywords：coal-gangue identificationmulti-source information fusionvibration signalsimage recognitionmulti-head attention mechanismmulti-layer long short-term memory model
Publication Date：2024-11-10
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 43-51 )