The Coal Production Anomaly Detection Based on Data Mining

Abstract:

Choose data Mining to study the anomaly detection in coal preparation, using ash of raw coal , rapid ash and yields of raw coal which density below 1.45, and ash and actual yields of fine coal in the database as sample attribute of coal production anomaly detection model, based on Box-plot analysis, the evaluating values range of five attribute above are determined. On this condition, by using SVM and KNN, the identification model of anomaly detection in coal preparation is established. The Receiver Operating Characteristic curves analysis result shows judging production target Abnormal Conditions using SVM will be more accurate in coal preparation.

• Periodical: Applied Mechanics and Materials (Volumes 239-240)
• Pages: 744-748
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.239-240.744
• Online since: December 2012
• Authors: Guang Hui Wang, Ya Li Kuang, Zhang Guo Wang
• Keywords: Anomaly Detection, Classification Algorithm, Coal Preparation, KNN, Support Vector Machine (SVM)

References

[1] Lu Maixi. Management of Technology in Coal Preparation Plant [M]. Xu Zhou: China University of Mining and Technology Press, 2005:41.

[2] Liang Xun. Data Mining Algorithms and Applications [M]. BeiJing: Peking University Press, 2006.

[3] Liu Yongkuo, Xie Chunli, Xia Hong. Research on Faults Data Processing and Attributes Reduction Arithmetic Based on Data Mining for Nuclear Power Plants[J]. Nuclear Power Engineering, 2010, (05):24-28.

[4] Kuang Yali, Deng Jianjun, Liu Huaiyu. Application of Artificial Interlligence in Field of Coal Preparation [J]. Journal of China University of Mining & Technology,2001,(06).

[5] Zhang haiyang, Deng jianjun, Yang chao et al. A Large-scale Coal Quality and Preparation Computer Comprehensive Management System for Coal Mining District-(Part 5) Coal Quality and Separation Information Comprehensive Inquire and Graph Analysis under Net Framework [J]. Coal Preparation Technology,2010,(01).

[6] Hu Bang-hui, Yuan Ye, Wang Xuezhong, et al. Thunderstorm prediction based on Bayesian classification method [J]. Journal of PLA University of Science and Technology(Natural Science Edition, 2010,(05).

[7] Gautam R S, Singh D, Mittal A, et al. Application of Svm on Satellite Images to Detect Hotspots in Jharia Coal Field Region of India [J]. Advances in Space Research, 2008, 41(11): 1784-1792.

DOI: 10.1016/j.asr.2007.05.011

[8] Chen Ping, Qiao Xiuquan, Liu Zhen, et al. Design and Performance Analysis of a Parallel Decision Tree Algorithm on Data Mining Grid [J]. Journal of Beijing University of Posts and Telecommunications, 2009, (S1).

[9] Cai Zhi-qiang, Sun Shu-dong1; Bernard Y, et al. Conditional Bayesian network classifier and its application in product failure rate grade indentifying [J]. Computer Integrated Manufacturing Systems, 2010, (02).

[10] Wang Zhangguo. Reaearch on Coal Quality, Standardization of Coal Preparation Information and Data Mining [D]. Xuzhou: Jiangsu Province,2009, 71-88.