Coal Mine Safety Investment Prediction Based on Support Vector Machine

2009 ◽  
Author(s):  
Chen Xiang ◽  
Cai Weihua ◽  
Chen Na
Keyword(s):  
Support Vector Machine ◽  
Coal Mine ◽  
Mine Safety ◽  
Support Vector ◽  
Coal Mine Safety ◽  
Safety Investment

Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector

2011 ◽  
Vol 135-136 ◽  
pp. 547-552
Author(s):  
Yuan Bin Hou ◽  
Ning Li ◽  
Fan Guo ◽  
Jing Chen
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Kernel Function ◽  
Coal Mine ◽  
Fuzzy Theory ◽  
Kernel Functions ◽  
Mine Safety ◽  
Support Vector ◽  
Coal Mine Safety ◽  
Rubber Belt

Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

scholarly journals Fuzzy comprehensive evaluation of coal mine safety investment structure based on the M(1,2,3) model

2012 ◽  
Vol 16 ◽  
pp. 592-597 ◽  
Author(s):  
Bai-Sheng Nie ◽  
Peng-Fei Zhao ◽  
Jian-Hua Guo ◽  
Peng-Peng Niu ◽  
Guo Wang
Keyword(s):  
Coal Mine ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Mine Safety ◽  
Coal Mine Safety ◽  
Investment Structure ◽  
Safety Investment

scholarly journals Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenming Sun ◽  
Dong Li
Keyword(s):  
Coal Mine ◽  
Evaluation Model ◽  
Weighted Least Squares ◽  
Evidence Theory ◽  
Safety Evaluation ◽  
Mine Safety ◽  
Sensor Data ◽  
Support Vector ◽  
Coal Mine Safety ◽  
High Conflict

Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

Analysis on Coal Mine Safety Status in China and its Countermeasures

2013 ◽  
Vol 448-453 ◽  
pp. 3814-3817 ◽  
Author(s):  
Chao Wang ◽  
Cheng Liang Zhang ◽  
Lei Liu
Keyword(s):  
Coal Mine ◽  
Safety Training ◽  
Coal Industry ◽  
Mine Safety ◽  
Training And Education ◽  
Coal Mine Safety ◽  
Geological Conditions ◽  
Safety Production ◽  
Prevention Method ◽  
Safety Investment

Aiming at actual situation of coal mine safety in China, the main problems in coal mine including geological conditions, scientific support and safety investment, shortage of technicians, safety training and education, and supervision laws were respectively analyzed, and the preventive countermeasures such as strengthening research of mechanism and prevention method of coal mine accidents, increasing safety investment, improving safety training and education, closing or integrating unqualified small coal mines, strengthening safety production supervision and perfecting legal system should be adopted to improve safety status of China coal industry.

scholarly journals A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877744 ◽  
Author(s):  
Peng Chen ◽  
Yonghong Xie ◽  
Pei Jin ◽  
Dezheng Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Swarm Intelligence ◽  
Coal Mine ◽  
Safety Data ◽  
Mine Safety ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Gas Poisoning

As the integral part of the new generation of information technology, the Internet of things significantly accelerates the intelligent sensing and data fusion in different industrial processes including mining, assisting people to make appropriate decision. These days, an increasing number of coal mine disasters pose a serious threat to people’s lives and property especially in several developing countries. In order to assess the risks arisen from gas explosion or gas poisoning, wireless sensor data should be processed and classified efficiently. Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. Using the popular deep neural networks, convolutional neural network and long short-term memory model, as comparisons, a number of experiments are carried out on several UCI machine learning datasets with different features. Experimental results show that least squares support vector machine optimized by swarm intelligence techniques can effectively handle classification task on different datasets especially on those datasets with limited samples and mixed attributes. The application of least squares support vector machine optimized by swarm intelligence techniques on real coal mine data demonstrates that this algorithm can process the data accurately and timely, therefore can warn of the accidents early in mining workplace.

Study and analyze the development of China coal mine safety management

2017 ◽  
Vol 11 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Lian-jiang Wei ◽  
Jian-kun Hu ◽  
Xin-rong Luo ◽  
Wei Liang
Keyword(s):  
Coal Mining ◽  
Coal Mine ◽  
Safety Management ◽  
Coal Mines ◽  
Coal Industry ◽  
Mine Safety ◽  
Content Type ◽  
Coal Mine Safety ◽  
Safety Production ◽  
Safety Investment

Purpose The purpose of this paper is to devise novel methods for effectively reducing China’s coal mining accidents via analysis of the relation between coal mine safety production and social factors. Design/methodology/approach The variations and characteristics of the safety production for coal mines in China from 1949 to 2013 are studied via induction and statistical analysis of data from the perspective of mortality rate per million tons, raw coal output and death tolls. It is analyzed that the relationship between coal mine safety production level and social economic, safety investment via SPSS. Findings Analysis of the coal mine safety management evolution across the 64 years after the founding of China demonstrates that China’s coal mine safety management evolution is partitioned into four stages, and there is the coupling relation between coal mine safety production and structure the of coal industry, government supervision and safety investment. By discussing the similarity between China and America in coal mine safety management evolution, it is found that the rapid increase in the number of accidents during the transformation from agricultural to industrial society is not accidental. Practical implications The suggestions in this paper are helpful to improve the current safety situation in China’s coal mines and provide management experience to other coal mining countries. Originality/value Based on present and future socioeconomic development, it is proposed that the coal mine safety situation can be further enhanced by properly adjusting the structure of the coal industry, strengthening supervision and ensuring safety investment.

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