Multipoint Acceleration Information Acquisition of the Impact Experiments Between Coal Gangue and the Metal Plate and Coal Gangue Recognition Based on SVM and Serial Splicing Data

Impact contact behaviors with different velocities, direction and contact states occurred in the process of coal drawing between coal gangue particles and the hydraulic support greatly increase the difficulty of coal gangue recognition in top coal caving. In order to finally realize coal gangue recognition in top coal caving, this paper studied the impact behavior of a single particle coal gangue and the metal plate at any position and the problem of the identification of coal gangue particles. First, the any position impacting test bench and test system is constructed, in which multipoint acceleration information acquisition space is built. Second, 2400 groups of random impact tests between coal gangue particle and the metal plate are conducted. Third, while each impact test was going on, acceleration signals in the seven different positions of the metal plate were collected. Forth, after all the impact tests are completed, signals are processed by segmentation and re-sorted by randomization. Then, the method of signals serial splicing is proposed to process the different position standardized signals obtained in each group of impact test, and coal gangue recognition is proposed based on processed signal data and SVM algorithm. After that, the effect of parameters on coal gangue recognition accuracy of SVM algorithm is studied, and the corresponding parameter settings of SVM are accordingly determined. Finally, coal gangue recognition was conducted by using SVM based on different data samples, the influence law of the training set data set expansion method and the test set data matching relationship, the number of tandem sensors and matching relationship, and the number of training set samples on coal gangue recognition accuracy were studied. The study will serve as the research basis for the application of SVM and signals serial splicing method in coal gangue recognition.