| 矿井通风机振动故障诊断 |
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| 引用本文： | 李春华,肖洋.矿井通风机振动故障诊断[J].黑龙江科技学院学报,2006,16(4):237-240. |
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| 作者姓名： | 李春华 肖洋 |
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| 作者单位： | 黑龙江科技学院,电气与信息工程学院,哈尔滨,150027 |
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| 摘 要： | 针对矿井通风机振动参数难以测定和故障率较高的特点,利用神经网络建立了通风机振动故障诊断模型.采用BP算法,选择合适的训练步长及动量因子,使网络训练速度加快.研究表明:BP神经网络对被控对象的数学模型依赖程度较低,能够很好的实现通风机的故障诊断,为非线性系统的控制提供了一种行之有效的研究方法.
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| 关 键 词： | 通风机 故障诊断 神经网络 |
| 文章编号： | 1671-0118（2006）04-0237-04 |
| 收稿时间： | 2006-05-14 |
| 修稿时间： | 2006-05-14 |
Vibration fault diagnosis of mine ventilator |
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| LI Chunhua,XIAO Yang.Vibration fault diagnosis of mine ventilator[J].Journal of Heilongjiang Institute of Science and Technology,2006,16(4):237-240. |
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| Authors: | LI Chunhua XIAO Yang |
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| Institution: | College of Electric and Information Engineering, Heilongjiang Institute of Science and Technology, Harbin 150027, China |
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| Abstract: | This paper is directed at the characteristics of vibration parameters more difficult to determine and faults more likely to occur. The paper introduces the use of back-propagation neural network to diagnosis the vibration of mine ventilator, under analyzing the fault reason and fault characteristic. The results prove that the back-propagation neural network shows less dependence on the mathematical model of controlled object and provides one effective research technique for the non-linear control system. |
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| Keywords: | ventilator fault diagnosis neural network |
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