The gas drainage effect is one of the important elements in the study of gas drainage in coal mines. It is critical to establish an effective evaluation model of the gas drainage effect for coal mines because the result of gas drainage is directly related to the safety of the coal mines. Through the research related to the safety evaluation in the existing coal mining process, we discovered that there are few studies on the evaluation of the impact of deep and soft gas drainage, and the evaluation methods are not sufficiently effective to resolve the complex problems arising in the process of gas drainage. This paper took “three soft” coal seams in the Lugou Coal Mine as the research object and constructed the evaluation index system on the basis of thoroughly analyzing the factors of coal seam drainage. We then employed a combination weighting method to attain the optimal weight by organically integrating the Analysis Hierarchical Process subjective weighting method and the Criteria Importance Through Interaction Correlation objective weighting method and utilized the cloud model to compute the numerical characteristic value of the evaluation index. In the end, this method obtained an evaluation result of the gas drainage effect evaluation. The evaluation result grade is good. Additional analysis was performed according to the evaluation factors, and corresponding improvement measures were proposed. This is of great importance in promoting safe production and improving the efficiency of gas drainage.

Nowadays, underground coal mine accidents occur frequently, causing huge casualties and economic losses, most of which are gas explosion accidents caused by fires. In order to improve the emergency rescue capability of coal mine fires and reduce the losses caused by coal mine fires, this article is dedicated to the assessment of coal mine fire rescue capability. Taking the fire emergency rescue system of Lugou mine as an example, based on the introduction of gray system theory and gray evaluation method, an evaluation model was established to assess the risk of the fire emergency rescue index system of Lugou mine. Four primary and 19 secondary indicators were delineated, and a hierarchical structure model of the fire emergency rescue capability of the Lugou mine was established by combining expert opinions, and the weights of indicators at all levels were calculated by using hierarchical analysis. We then used the gray system evaluation method and expert scoring to judge the safety level of various indicator factors in the index system. The evaluation results show that the risk level of the emergency rescue system of the Lugou mine fire is higher than the fourth level. The main risk indicator factors are firefighting equipment, decision-making command, emergency education and training, and fire accident alarm. In response to this evaluation result, corresponding control measures were formulated in four aspects: rescue organization guarantee, personnel guarantee, material guarantee, and information guarantee, which optimally improved the emergency rescue capability of the Lugou mine fire and reduced the loss caused by fire.

The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis.

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