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| 用小波网络法预测提升机故障趋势 | |
| 引用本文： | 彭观明,李志强,刘照军.用小波网络法预测提升机故障趋势[J].煤矿机械,2007,28(2):180-182. |
| 作者姓名： | 彭观明 李志强 刘照军 |
| 作者单位： | 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 |
| 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。 |
| 关 键 词： | 提升机 小波网络 故障预测 |
| 文章编号： | 1003-0794（2007）02-0180-03 |
| 修稿时间： | 2006-11-03 |
Trend Estimate of Hoist Fault with Wavelet Neural Network |
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| PENG Guan-ming,LI Zhi-qiang,LIU Zhao-jun.Trend Estimate of Hoist Fault with Wavelet Neural Network[J].Coal Mine Machinery,2007,28(2):180-182. | |
| Authors: | PENG Guan-ming LI Zhi-qiang LIU Zhao-jun |
| Abstract: | 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. |
| Keywords: | mine hoist wavelet neural network fault forecasting |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |