Application of Artificial Intelligence Technology to Coal Mine Machinery Fault Diagnosis

Abstract:

With the development of network and intelligent technology, artificial intelligence technology was applied to fault diagnosis of coal mine equipment maintenance. Using the artificial intelligence technique for fault diagnosis of coal mine machinery, not only the accuracy of coal mine machinery fault diagnosis can be improved, and also a positive significance of the use of the normal operation of machinery and equipment is guaranteed in coal mine. In this paper, the ventilator equipment in the coal mine will be taken as an example for analyzing the application of artificial intelligence technology to coal mine machinery fault diagnosis, in order to improve the maintenance of mine ventilation work efficiency and guarantee the normal operation of the application in the production of coal mining.

• Periodical: Applied Mechanics and Materials (Volume 686)
• Pages: 174-179
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.686.174
• Online since: October 2014
• Authors: Jie Li
• Keywords: Artificial Intelligence Technology, Coal Mine, Fault Diagnosis, Machinery, Mining Production

References

[1] Lijin YANG, Ye YANG, Jingyi TIAN. Intelligent Fault Diagnosis Methods for the Large-scale Complex Equipment [J]. Journal of Coal Mine Machinery, 2012 (1).

[2] Dong SONG, Hong-xia PAN. DSP-based Intelligent Fault Diagnosis System for Heading Machine [J]. Journal of Coal Mine Machinery. 2011 (6).

[3] Changlin WANG, Wei LIN, Hongbao CHEN, Qimao QIN, Yimei SONG. Study on the Machinery Multi-fault Intelligent Classification Method based on Support Vector Machine (SVM) [J]. Journal of Coal Mine Machinery, 2009 (10).

[4] Jian WU, Nan WU. Study on the key Fault Detection Technology for Coal mine Mechanical and Electrical Equipment based on Wavelet Analysis [J]. Automation and Instrumentation, 2011 (5).

[5] Qifeng FU, Yanpin CUI, Xiongwei GE. The Research and Development of the Real-time Variable Frequency Pump State Monitoring and Intelligent Fault Diagnosis System [J]. Journal of Coal Mine Machinery, 2005 (6).

[6] Yanqiao WANG, Ziming GUAN. Study on the Belt Conveyor Roller Bearing Condition Monitoring and Fault Diagnosis [J]. Mechanical Management and Development, 2009 (1).

[7] Xiongfei LI, Junjie XUN, Lei CHEN. Study on the Mechanical Fault Diagnosis Combining Double Spectrum with Support Vector Data Description [J]. Journal of Coal Mine Machinery, 2012 (2).

[8] Zengqiang WANG, Wanli CHE, Zhenlin QUAN, Xuhui ZHANG, Hongwei MA. Study on the Online Monitoring and Fault Diagnosis System for Electric Traction Shearer [J]. Heavy Machinery, 2012 (5).

[9] Jiangang LI, Zihui REN, Renxia LIU. Study on the Mine Ventilator Fault Diagnosis based on Elman Neural Network [J]. Coal Mine Machinery, 2011 (8).