用小波网络法预测提升机故障趋势  

Trend Estimate of Hoist Fault with Wavelet Neural Network

作　　者：彭观明[1] 李志强[2] 刘照军[3] 

机构地区：[1]泰山学院,山东泰安271000 [2]泰山职业技术学院,山东泰安271000 [3]泰山医学院,山东泰安271000

出　　处：《煤矿机械》2007年第2期180-182,共3页Coal Mine Machinery

摘　　要：采用小波网络方法,通过对矿井提升机钢丝绳磨损度、空动时间、衬垫磨损寿命、闸瓦间隙、残压和制动盘偏摆度等关键特征参数的时间序列预测,实现了特征参数的故障预报。仿真和实验结果表明,预报精度满足要求,对保证矿井提升机安全和高效运行具有重要意义。Adopting wavelet neural network, fault forecast of characteristic parameters of mine hoist is realized by forecasting time series of key characteristic parameters of mine hoist that include abradability 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. Emulation and experiment result show that the forecast accuracy of the wavelet meets demand and has an importance meaning to guarantee the inine hoist circulate efficiently safety.

关 键 词：提升机 小波网络 故障预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TD53[自动化与计算机技术—控制科学与工程]