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基于声发射和小波神经网络的机械密封状态分类新方法
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中央高校基本科研业务费专项资金项目(SWJTU12CX039);国家重大科技成果转化项目.


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    摘要:

    采用声发射方法监测得到的复杂机械密封的声发射信号往往信噪比很低,对其工作状态进行分类存在一定的困难。提出一种基于声发射和小波神经网络的机械密封工作状态分类的方法。该方法将小波与神经网络结合,基于声发射信号时域和小波包能量分析的特征提取方法,充分利用声发射信号中的有用信息,能很好地表征机械密封的工作状态。以旋转轴用动密封装置为例,采用上述方法对其工作状态进行监测。实验证明,该方法能够有效地对复杂机械密封的工作状态或故障类型进行分类。

    Abstract:

    The signal acquired by acoustic emission method to monitoring the complex mechanical seals usually has a low SNR signal, which makes the classification of the working condition of mechanical seals difficult. A new method was presented to classify the condition of mechanical seals based on acoustic emission and wavelet neural network. This method was combined of the wavelet and neural network, and extracted characteristics based on time domain and wavelet pack analysis to make full use of the useful information in acoustic emission signal, which could represent the working condition of mechanical seals well. With an experiment on the dynamic seals for rotating shaft, the method mentioned above was used to monitor the working condition. The experiment proves that this method can classify the working condition and fault pattern of complex mechanical seals effectively. 

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林志斌,傅 攀,张尔卿,李晓晖,黄泽沛,陈 侃.基于声发射和小波神经网络的机械密封状态分类新方法[J].润滑与密封,2014,39(9):40-45.
.[J]. Lubrication Engineering,2014,39(9):40-45.

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  • 在线发布日期: 2014-11-25
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