Abstract
In order to realize the recognition of coal gangue in the top coal caving process, a scheme of the coal gangue recognition based on the collision vibration signal between coal gangue and the metal plate is proposed in this paper, a systematic and standardized impacting test between coal gangue particles and the metal plate is designed for the first time, the vibration signal standardized processing method by the signal intercepting and the coal gangue impact vibration signal recognition algorithm by stacking integration are innovatively proposed. First, a single particle impact on the metal plate test-bed was designed and constructed. Then 1,000 groups coal and 1,000 groups gangue impact on the metal plate tests were carried out respectively, and the vibration acceleration signals of the metal plate were collected. After that, through the signal intercepting, calculating the time-domain characteristics and HHT processing of the vibration signal, 10 time-frequency characteristics, such as the variance of the intercepted signal and the Hilbert marginal spectrum energy value, are determined to form the feature vector. Finally, based on the two different type of the signal samples, the intercepted signal feature vector, and the original intercepted signal, coal gangue recognition by the seven machine learning algorithms, including the decision tree (DT), random forest (RF), XGBoost, long short-term memory (LSTM), support vector machine (SVM), factorization machine (FM), and stacking integration is carried out respectively, and the basis for selecting recognition schemes is discussed. The results show that the coal gangue recognition rate with the same recognition algorithm by using the intercepted signal samples is higher than that of the feature vector samples, the Staking integration algorithm based on the same sample has the highest recognition rate, and the Staking integration algorithm based on the feature vector has the most significant comprehensive advantage in top coal caving process.
Highlights
Coal which has dual attributes of energy and resources is the main energy source in China [1]–[3]
According to the previous research achievements on the spherical contact and the spherical plate contact [45]–[60], and after the collision and slippage behavior analyze between the coal gangue and the metal plate of the hydraulic support in the process of top coal caving, we have studied the systematic dynamic and contact response for the rock sphere elastic impact on the metal plate and for the coal ganguelike elastic spherical particle impact on the elastic half-space [61], [62], and preliminarily proved the exist of the difference between the contact responses and the vibration signals when coal gangue impact on the metal plate respectively, which explains the identifiability of coal and gangue
This paper mainly studies the vibration signal of metal plate after it is impacted by single particle coal and gangue, so the test data to be collected is the vibration signal of metal plate
Summary
Coal which has dual attributes of energy and resources is the main energy source in China [1]–[3]. VOLUME 7, 2019 signals of the tail beam of the hydraulic support under different conditions of caving coal, caving gangue and caving roof with a portable vibration data recorder developed by themselves, and put forward the coal-rock recognition method based on the energy analysis to wavelet packet frequency bands [25]. Based on the above research, and in order to break through the problem of the coal gangue surface identification in the top coal mining, we innovatively put forward to disassemble the complex coal gangue recognition problem in top-coal caving, adopt the approach of breaking up the whole into parts and research from point to face, and took the single particle coal gangue recognition as a breakthrough point, through constructing the vibration testbed of single particle impacting metal plate and conducting large sample single particle coal gangue impact test, combined with signal acquisition, signal interception, Hilbert-Huang transform and other processing methods, as well as machine learning and deep learning and other classification methods, the identification of the single particle coal gangue based on the impact vibration signal of metal plate is completed and the recognition scheme is discussed considering the different recognition sample data and the specific application requirements.
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