Trend Forecast of Mine Hoist Fault with Wavelet Neural Network
The wavelet neural network is used to predict the time series of key characteristic parameters about the abrad-ability of steel wire rope, time of idle motion, life of pad wear away, clearance of brake shoe, remnant oil pressure and deflection degree of brake disk for the mine hoist Then the trend of hoist fault can be forecasted from these predicted characteristic parameters. Simulations and experiments indicate that the forecasting precision can satisfy the practical requirement It is very significant to ensure the secure and efficient run of mine hoist.
mine hoist wavelet neural network fault forecast
Liu Zhaojun Zhu Xijun
College of Information and Engineering, Taishan Medical University, Taian 271016, China School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao
国际会议
The 3rd International Symposium on Modern Mining & Safety Technology Proceedings(第三届现代采矿与安全技术国际学术会议)
辽宁阜新
英文
1050-1053
2008-08-04（万方平台首次上网日期，不代表论文的发表时间）