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.

The scraper conveyor is the unique transport equipment in coal mining face. Its chain drive system is the core subsystem. The dynamic characteristics of the chain drive system need monitoring for a safe mining. Existing monitoring methods include controlling the chain tension, chain speed and motor current. However, the above-mentioned methods have many disadvantages, such as difficult and costly to implement in mining face, due to the poor working environment and the limitations of the scraper conveyor’s structure. This study proposes a monitoring method for the dynamic characteristics of the scraper conveyor on the basis of the speed difference between the head and the tail sprockets. Such an initiative is carried out to study the dynamic characteristics of the scraper conveyor under real working conditions and describe its running state. The speed difference of the head and tail sprockets under different chain speeds, terrains, and loads is monitored and studied. Research results show that the average and maximum speed differences are 0.797% and 0.990%, respectively. Due to the complex terrain, the tighter the scraper chain gets, the smaller the difference of the rotational speed is, compared with the flat working condition. The speed difference under load greatly increases compared with no load. Research results provide data support for the research of the chain drive system dynamics of the scraper conveyor. These results will also provide a new idea for the monitoring of the operating state of the scraper conveyor.