Coal-rock property identification based on multi-source information fusion

Coal–rock property identification during cutting mainly includes the recognition of process signals generated during cutting, infrared imaging, image feature recognition, and reflectance spectrum identification. Owing to the complex environment of roadway excavation, noise generated during the operation of a roadheader-bolter integrated machine easily overwhelms the acoustic signals produced by cutting, while vibration signals are also susceptible to interference from the machine's own vibrations. Methods based on infrared imaging and image recognition are easily affected by high dust concentration and low illumination during excavation, resulting in poor performance in practical working faces. Meanwhile, identification methods based on a single sensor are limited by environmental complexity and the identification range. To address these issues, a multi-source information fusion identification method based on acoustic and vibration signals was proposed. First, considering the complex underground environment with strong noise interference, the Variational Mode Decomposition (VMD) algorithm was used to decompose acoustic and vibration signals, and a Shannon entropy-based mode selection criterion was proposed to reconstruct and denoise the signals, thereby obtaining effective acoustic and vibration signals. Then, a Markov Transition Field (MTF) was employed to transform the acoustic and vibration signal feature sequences into two-dimensional images for feature fusion. Finally, a Convolutional Neural Network (CNN) combined with a Convolutional Block Attention Module (CBAM) was introduced to perform spatial feature learning on the two-dimensional images, enabling automatic extraction of multi-scale features and precise enhancement of key features, and thus achieving accurate identification of coal–rock properties during cutting. Experimental results showed that the proposed method achieved an identification accuracy of 99.4048%, which was significantly higher than that of traditional CNN models.