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
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;
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
DOI :
10.1109/APPEEC.2009.4918342