Research on Key Technology of Coal and Rock Cutting State Identification

Abstract

This paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and the deep learning theory. It focuses on the key technologies of the coal and rock cutting state identification, such as "the acquisition and processing of the coal and rock cutting state data information set, sample expansion, identification and classification". The EDEM and RecurDyn were used to build the high-precision three-dimensional simulation model of complex coal seam and the bidirectional coupling model between shearer cutting parts, and the simulation group was set based on the actual cutting state of the shearer. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into the time-frequency images by STFT, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. The basic database for coal and rock cutting state recognition was also constructed to provided original samples with rich and precise characteristic information for the expansion of recognition system data. Based on the deep learning theory, a DCGAN-RFCNN recognition network model with strong ability to perceive the cutting state information of coal and rock and rapid recognition and classification was constructed. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.