• DocumentCode
    3352972
  • Title

    Evaluation Coupling Model of Mine Ventilation System Based on RS and ANN

  • Author

    Fu Yuhua ; Dong Longjun

  • Author_Institution
    Sch. of Resources & Safety Eng., Central South Univ., Changsha
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The broad masses of coal mining enterprises have been very concerned about how to effectively evaluate safety and reliability of mine ventilation system. Domestic and international scholars in this regard have also carried out a substantial amount of research and made a variety of different types of evaluation methods, but because there are many complex factors that affect the safety and reliability of mine ventilation system, it is very difficult to evaluate the ventilation system accurately using traditional methods. In view of this, this paper established an evaluation system based on rough sets and artificial neural network theory (ANN), it can complete a multi-level and multi-factor evaluation system and have self-learning capabilities. The coupling model is tested in actually projects, it shows that the model has a high accuracy, so that the model can be applied to the safety evaluation at the scene.
  • Keywords
    coal; mining industry; neural nets; occupational safety; production engineering computing; reliability; ventilation; ANN; RS; artificial neural network; coal mining enterprises; evaluation coupling model; mine ventilation reliability; mine ventilation safety; mine ventilation system; multi-factor evaluation system; multi-level evaluation system; self-learning capabilities; Artificial neural networks; Domestic safety; Fans; Fuzzy systems; Layout; Reliability engineering; Rough sets; Set theory; Testing; Ventilation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918342
  • Filename
    4918342