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WANG Haoyu, CHEN Ying, MIAO Yanzi, CHEN Bingguang. A fault diagnosis system of mine main ventilator[J]. Journal of Mine Automation, 2017, 43(6): 69-71. DOI: 10.13272/j.issn.1671-251x.2017.06.016
Citation: WANG Haoyu, CHEN Ying, MIAO Yanzi, CHEN Bingguang. A fault diagnosis system of mine main ventilator[J]. Journal of Mine Automation, 2017, 43(6): 69-71. DOI: 10.13272/j.issn.1671-251x.2017.06.016
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A fault diagnosis system of mine main ventilator

  • School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

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  • A fault diagnosis model was built by use of neural network trained by extreme learning machine. A fault diagnosis system of mine main ventilator based on the model was designed, and software and hardware design schemes of the system were introduced. The test results show running time of extreme learning machine algorithm in the system is only 0.031 3 s and accuracy rate of fault diagnosis is not less than 97.35%, which has better real-time performance and accuracy than fault diagnosis systems based on BP neural network, ELMAN neural network or neural network trained by support vector machine.
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