1. Introduction
In recent years, although the safety situation of coal mines in China has improved, serious accidents still occur from time to time. China’s coal mine mortality per million tons is now about 0.156, down from previous years, whereas the mortality per million tons in Australia, the United States and other coal-producing developed countries ranges from 0.03 to 0.04. Obviously, there is still a huge gap between them [
1,
2].
Coal mining has narrow and changeable working face space with much equipment, loud noise, concentrated personnel and a complex environment. Additionally, there are many uncertain risk factors, and mine accidents occur frequently. According to the statistics of The State Bureau of Safe Production Supervision and Administration of China, 45% of all coal mine accidents were attributed to coal mining faces or accidents induced by them [
3].
Safety early warning system is an important means to prevent and control the occurrence of hidden dangers of coal mine accidents, which has been paid close attention by scholars. From the late 1990s, some scholars started researching coal mine safety warnings. According to Lei’s research, accidents from hidden dangers were derived from hazardous and harmful factors, while inherent hazardous and harmful factors were the essential attributes of hazard sources [
4]. Safety early warning was to collect data of all kinds of accident hidden dangers, evaluate, analyze and monitor safety production activities, and issue different early warning signals at different stages [
5]. In the process of coal mining, if the hidden dangers are eliminated, the hazard sources can be controlled, and the occurrence of accidents can be avoided or reduced. The coal mine safety early warning is mainly carried out by hidden trouble control, major hazard identification and safety evaluation, safety monitoring index early warning, and so on. From the perspective of coal mine hidden danger management, some scholars have carried out research on hidden danger classification and investigation management, such as Tan Zhang-lu Tan Zhanglu et al. who designed a closed-loop management system for safety early warning and inspection in two links of risk source management and hidden danger management [
6], and Wang applied an LEC method to study hidden security classification of closed loop management [
7]. Most of these studies regarded the hidden danger data as static data and take the historical hidden danger data as the research basis. It could provide some reference for the investigation of hidden danger, but it didn’t conform to the characteristics of the dynamic change of hidden danger [
8], and it was difficult to provide real-time warning information or change trend of hidden danger. The research on major hazard sources focused on the analysis and identification of hazard sources and the construction of risk assessment models. For example, Chen et al. established a risk assessment model, which took geographical environment, technical equipment, personal quality, safety training and management as variables [
9]. Luo et al. carried out quantitative risk assessment on gas explosion risk [
10]. Zhang and Sui et al. proposed measures to prevent and control coal mine water hazard sources [
11,
12]. These studies established one kind of mine disaster, such as gas or a flood risk assessment model, can be one kind of prevention and control of hazards occur, but accident factors caused by coal mining area are very complicated, and can include several categories of hazards, and also people’s unsafe behavior and unsafe state related factors [
13]. In order to comprehensively identify the hazard source and objectively evaluate the mine safety situation, attention should be paid to the human, machine, environment and pipe, etc. aspects. As for the research on the early warning of safety monitoring indicators, the early warning model was mainly established based on the safety monitoring indicators of gas or other harmful gases concentration, hydrological parameters and roof pressure. For example, Li established the Elman neural network risk identification and early warning model of gas explosion disaster [
14], Wu et al. established a mathematical evaluation system of floor water inrush risk based on variable weight model and unascertained measure theory [
15], Dou et al. established a multi-information normalized early-warning mechanical model of coal-rock impact damage [
16], and Zheng, Qiao and Man established a remote monitoring and early-warning technology of mine hoist [
17]. These research warning indicators were relatively single, and have their own limitations [
18] in terms of application scope, evaluation accuracy, and objectivity of evaluation, etc., and the failure to effectively analyze nonlinear systems [
17] would lead to large warning errors. Through integration technology, several algorithms were fused together to establish an early warning model, which could play a role of promoting strengths and circumventing weaknesses [
18]. Therefore, some scholars have carried out research on the comprehensive early warning model of various indicators and established the integrated muti-algorithm early warning model. This includes the application of artificial neural network, fuzzy comprehensive evaluation method, expert system, rough set, support vector machine (SVM), data mining and other algorithms. Meng and Zhao comprehensively adopted an FCM fuzzy clustering algorithm, rough set and RBF neural network to establish data mining and data fusion to construct a coal mine safety early warning system [
19]. Xin and Sun, Wen, Wang and Cai, Wang et al., Chen et al., Liu et al. respectively, established different safety warning models by applying principal component analysis, neural network algorithm, extension theory and a support vector machine algorithm [
20,
21,
22,
23,
24]. Zhang, Jiang and Guo, Jia and Wei established a coal mine safety early warning system by using association rule data mining [
25,
26]. These studies not only give full play to the intelligent advantages of computers, but also avoid the one-sidedness of data indicators, increasing the rationality of the index system [
5,
26]. However, some comprehensive warning model algorithms were too complex to be operated easily in practical application. The principles of simplicity, applicability and strong operability need to be fully considered, and appropriate methods and models should be selected through comparison according to the target achieved by the research object, actual conditions and the environment [
27].
As an important subsystem of coal mine production, the coal mining production system can also adopt the ideas and methods applicable to coal mine safety early warning model, including safety monitoring and early warning of single hazard source index and intelligent safety early warning. Early warning of the working face safety monitoring system with single hazard source index was based on the monitoring data of roof pressure, hydrogeology, coal seam spontaneous combustion, gas, etc. For example, Fan, Mao and Qi realized early warning of roof disaster [
28]. Liu et al. studied the early warning index of monitoring coal seam floor [
29]. In addition, Shi et al. established an evaluation method for the threat degree of spontaneous combustion of the working face under the condition of coal seam group mining in close distance, so as to realize the advance warning of the threat of spontaneous combustion fire faced by the working face in close distance coal seam group mining [
30]. Due to the fuzziness, randomness, suddenness, non-linearity and dynamic characteristics of the coal face safety system [
31], it is difficult to guarantee the accuracy of the early warning system established solely by the safety monitoring system. Therefore, some scholars have carried out intelligent safety early warning represented by artificial neural networks in recent years. For example, Lu and Kang studied the principal component analysis combined with back-propagation neural network (BP) prediction model for the prediction of gas emission in mining face, Hao and Zhang studied the prediction and early warning method of coal and gas outburst with Hadoop platform, and established the BP neural network model to extract gas characteristic parameters early warning model [
32,
33]. Zhao and Ma established the BP neural network prediction model of particle swarm optimization for the prediction of dust concentration in fully mechanized mining face [
34]. These intelligent safety early warning systems based on BP neural network solve the problems of non-linearity and fuzziness of danger source and accident hidden danger of coal working face, but the single index of major danger source of coal working face is taken as the research object, and all kinds of danger source and accident hidden danger indexes of coal working face are not considered comprehensively. As a production system, the safety state of a coal mine is affected by personnel, equipment, environment, management, information and other factors. If one factor appears unsafe, it may affect the safety of other factors and cause accidents [
31]. Therefore, it is not possible to fully reflect the safety situation of coal face by studying only a single hazard source or ignoring the interaction between the hazard sources.
Therefore, it is necessary to design a set of intelligent model of coal face safety early warning, which covers all the warning indicators of human, machine, environment and management, so as to improve the ability of coal face risk pre-control and eliminate hidden dangers, reduce or avoid the occurrence of accidents, and reduce the death rate of coal mine accidents. Through literature review, the warning index is the basis of safety evaluation, construction of early warning model, the selection and design in the model, model quality affects the effect of application, a model can realize the original function, which is to think it is a good model, good model with simplicity, accuracy and explain the universality and operability of the three necessary conditions for [
35]. Therefore, when designing the coal face safety warning model in this study, the applicability of the theories and methods used should be considered first, and the practicability of the warning model should be paid attention to, rather than blindly pursuing novel or complex theoretical algorithms. The hidden dangers such as roof pressure, flood, fire, gas and dust of coal face are dynamic, uncertain and nonlinear, which are difficult to describe by an accurate mathematical early warning model. The BP neural network is one of the widely used neural network models. As for the complex enterprise operation system, it solves the defects of the traditional model, such as the difficulty to deal with highly nonlinear model, poor fault tolerance, lack of time variability in the early warning line and early warning area, and lack of self-learning ability, and shows a good prediction effect [
36].
BP neural network can arbitrarily approach a highly nonlinear function and has the ability of self-organization and self-learning. Moreover, BP neural network has strong nonlinear fault tolerance and allows strong noise in data, so it has great advantages in prediction and early warning [
37]. However, when BP neural network is trained by gradient descent method, it may produce local minimum problem, and there is also the sensitivity problem of the initial weight of the network, that is, any slight change in the initial weight will affect the convergence speed and precision of the network, and even the network vibration [
38,
39,
40]. A genetic algorithm (GA) has strong global search ability, which can quickly and effectively find the global optimal solution in the complex, multi-peak and non-differentiable large vector space, and has the characteristics of efficient, parallel and global search [
41]. A genetic algorithm is used to overcome the above defects of BP neural network.
Based on the comprehensive analysis of the production practice and the characteristics of safety hidden dangers of coal working face, this paper establishes a full warning index system based on personnel behavior, equipment, environment, management and other factors, and integrates the advantages of genetic algorithm and BP neural network to construct a safety early warning model of coal working face.
Fuzzy C-means (FCM) is an algorithm that uses membership degree to determine that each data point belongs to a certain degree of clustering [
41]. It converges the target clustering function through multiple iterations. With a FCM algorithm to cluster the input data, the network structure and initial parameter values can be determined to ensure uniform distribution of membership functions, so as to improve the convergence speed of the neural network. Therefore, FCM algorithm was introduced to process the hidden danger data of working face in fuzzy discretization and optimize the sample data space, so as to improve the operation efficiency and precision of the neural model.
The structure of the paper is roughly as follows: First, the characteristics of safe production of coal mining face are studied to develop a comprehensive early-warning index system composed of personnel behavior factors, environmental factors and management factors; Second, FCM fuzzy clustering is applied to fuzzy discretization of hidden danger data of the working face, and sample data state space is optimized to build an early warning model of coal mining face based on genetic algorithm and BP neural network; Finally, the effectiveness of the early warning model is verified by taking the concrete coal mining face as an example.
4. Result Discussion
The practical application results of a fully mechanized mining face in Shandong, China, show the feasibility of the early warning model. Combined with the output data and charts, the feasibility of artificial neural network early warning model, the effectiveness of BP neural network optimized by genetic algorithm, and the rationality of FCM clustering algorithm are further discussed.
4.1. Feasibility of the Warning Model
Figure 4 is analyzed to illustrate the prediction feasibility of the BP neural network early warning model.
In the full-index early warning model of the mine, the input layer of the neural network contains 25 neurons, the output layer contains one neuron, and the neurons of the hidden layer are finally confirmed to be 14. The function of the hidden layer and the incentive function of the output layer are S-shaped functions, the training function is trainlm, and the learning rate is 0.001.
The sample input network is repeatedly trained until the error is less than 0.00001.
Figure 4 is the output error performance curve, which shows that the training times of BP neural network reaching the error limit are 639, and the root mean square error is 5.7356 × 10
−20.
Figure 5 is the curve of the simulation results of the predicted data, which shows that the actual data curve is significantly different from the predicted curve, but the variation trend of the curve is basically the same.
The result analysis in
Figure 4 and
Figure 5 shows that the BP neural network model can achieve the expected error performance index and predict the index value, but the relative error is relatively large.
Figure 6 and
Figure 7 are analyzed to illustrate the feasibility of GA-BP neural network early warning model prediction.
The population size was set as 50, the genetic algebra was set as 100, the mutation probability was set as 0.3, the same BP neural network model was adopted, and the same training samples were used.
Figure 6 is the output error performance curve of GA-BP neural network, showing that the error limit is reached after 452 times of training.
Figure 7 is the curve of the simulation results of the predicted data. Similarly, the actual data curve is significantly different from the predicted curve, and the variation trend of the curve is roughly the same.
The result analysis in
Figure 6 and
Figure 7 shows that the GA-BP neural network model can achieve the expected error performance index, and can predict the index value, but the relative error is relatively large.
Within the relative error range of ±6%, the prediction hit rates of the two models shown in
Figure 4 and
Figure 5 are all below 50%. The fitting degree between the prediction curves of the two models and the real curves also shows that the prediction errors of the two models are relatively large, and the output results are not very ideal.
(1) The sample selected is not large enough.
Although the samples from 2007 to 2017 were selected, only 46 sets of data were collected in the third month of each quarter, so the sample size was too small.
(2) Quality of selected sample data.
The sample data is collected by questionnaire, which is sent to the personnel responsible for mining safety, mechanical and electrical, production, transportation, one and three prevention, and finally collected and sorted by the deputy general manager of mine mechanical and electrical.
Survey to the mine safety record in some data or missing, missing some data in the survey, the hard to avoid appear strong subjectivity, optional the gender is big, and the early stage of the quantitative and qualitative index of the existence very big subjectivity, some data can’t reflect objective safety state, and the sample data from the correlation between strong, also affect the quality of the output.
In order to overcome these defects in future research and application, the sample size should be large enough, the weight of each index should be calculated, and the unqualified indexes should be eliminated to ensure the quality of application effect.
4.2. Validity of Optimizing BP Neural Network Performance with Genetic Algorithm
In the same performance goals, and the same training samples,
Figure 4 reflects the traditional BP neural network training mature iteration number is 639,
Figure 6 reflects the genetic algorithm to optimize the BP neural network training after mature iteration number is 452, a 1.4 -fold increased convergence speed, shows that GA-BP faster convergence than BP neural network to the requirements of accuracy,
It shows that genetic algorithm can improve the performance index of BP neural network.
In terms of output error performance index, the total output error of GA-BP training samples is 5.85 × 10−6 and the average output error is 2.81 × 10−10, while the total output error of BP neural network training samples is 8.18 × 10−6 and the average output error is 3.92 × 10−10, and the average output error of the latter is 1.39 times of the former.
The curve of simulation results of prediction data shows that the errors shown in
Figure 5 and
Figure 7 are relatively large, but the overall fluctuation of GA-BP algorithm error represented in
Figure 7 is smaller than that of BP algorithm, and the overall prediction effect of GA-BP algorithm is more ideal.
Through comparative analysis, it is shown that the introduction of genetic algorithm to optimize the initial connection weights and thresholds of the neural network makes the BP neural network converge faster, reach the mature state and enter the prediction stage faster, which indicates the feasibility and effectiveness of the optimization of the BP neural network by genetic algorithm.
Table 9 is analyzed to illustrate the effectiveness of GA-BP neural network in predicting and warning the safety conditions of coal face.
The operation results of the forecast data in March 2007 in the warning model show that the safety condition of the coal face in that month is not good, and the safety level is low.
After consulting the actual safety records of the mine in March 2007, it is found that the safety personnel allocation rate, coal dust control, roof control and flood control safety evaluation are all below 0.7, and the safety input ratio is below 0.7.
According to the geological data of the coal working face of the mine, “explosion index is 37.45%, which is explosive”, and the integrity rate of coal dust prevention and control in that month is 0.559. According to the Coal Mine Safety Regulations, the coal dust concentration of the coal working face shall not be higher than 10 mg/m3, and the dust removal rate of the dust prevention effect shall not be less than 90%, that is, the integrity rate reaches more than 0.9.
In March 2007, the coal dust concentration in this mine was seriously out of standard, and there was a risk of coal dust explosion. The prediction results of the early warning model were consistent with the actual safety conditions of coal mining face, which indicated the effectiveness of the prediction and early warning of GA-BP neural network.
4.3. Rationality of Application of FCM Clustering Algorithm
The results of
Table 3 and
Table 4 and
Figure 3 were analyzed to illustrate the rationality of FCM clustering algorithm.
The FCM clustering algorithm discretized the original sample data in order to optimize the sample space and improve the operation efficiency of the neural network.
Table 3 is the optimal central vector of FCM clustering operation for sample data.
Table 4 is the result of FCM clustering, which shows that the sample data is divided into three sets after clustering.
Figure 4 shows the fuzzy set of samples after clustering more intuitively.
The FCM clustering samples were used for neural network training and testing with MATLAB. Due to the elimination of some interference samples, the efficiency of selecting training samples and testing samples in the neural network system was improved, thus effectively reducing the training time of BP neural network.
4.4. The Possibility of Application in Intelligent Coal Mine
At present, intelligent mining has become the focus of construction in China. The classification and grading evaluation index system and evaluation method of intelligent coal mines can evaluate the type of mine intelligent construction conditions and the level of mine intelligent construction. The evaluation results also reflect the shortcomings of mine intelligent construction and provide guidance for the subsequent intelligent upgrading and transformation of coal mines [
64]. The safety early warning model constructed above can evaluate the safety condition of the coal working face by classifying the hidden danger index of the coal working face and by using the fuzzy clustering algorithm and GA-BP neural network operation. Therefore, the model can provide help for the intelligent mining of coal mine.
5. Conclusions
The quality of the early warning indicators is the key to the operation effect of the early warning model. “The early warning elements should include all the dangerous states that may lead to accidents. In order to achieve no omission and alarm leakage, it is necessary to carry out repeated investigation of dangerous elements for six major specialties (coal mining, excavation, transportation, ventilation, electromechanical, water prevention and control) and three analysis objects (personnel, process and device) in coal mine” [
65]. Compared with previous researches on safety warning indicators and warning models of coal mining face.
The whole warning index system of “human, machine, environment and pipe” and other dangerous elements established in this paper makes the analysis and evaluation of the safety state of coal face more comprehensive and systematic.
On the basis of the security warning index, a security warning model combining the advantages of genetic algorithm and BP neural network was established, and FCM clustering algorithm was used to optimize the network sample space, thus improving the network learning speed.
Taking a fully mechanized mining face in a mine as an example, the results of the early warning are verified to be consistent with the reality, which indicates that the effectiveness of improving the weight and threshold of BP neural network system by genetic algorithm can make up for the deficiency of BP neural network falling into local optimum, improve the intelligent search ability of the network, and improve the accuracy of the prediction results.
The BP neural network optimized by genetic algorithm has the advantages of the two, improves the stability of the model, and provides a method for the system with uncertainty, fuzziness and complexity, which is suitable for the safety early warning system of coal face.
Due to “coal mine safety production relative risk exists obvious spatial distribution and the provinces there is a big difference interval,” [
66] the coal mining face in different areas of the early warning index of different kinds of each index is different on the contribution rate of safety early warning effect, this article does not consider the weight of each index, and it is also a need to further research content. The application of this model in large industrial environment will be the focus of future research.