| 张小志,赵立宏,邓骞,宋超.小波包分析在乏燃料剪切机故障诊断中的应用[J].南华大学学报(自然科学版),2014,28(1):69~73.[ZHANG Xiao-zhi,ZHAO Li-hong,DENG Qian,SONG Chao.Application of Wavelet Packet Analysis in Fault Diagnosis of Spent Nuclear Fuel Shears[J].Journal of University of South China(Science and Technology),2014,28(1):69~73.] |
| 小波包分析在乏燃料剪切机故障诊断中的应用 |
| Application of Wavelet Packet Analysis in Fault Diagnosis of Spent Nuclear Fuel Shears |
| 投稿时间：2013-11-11 |
| DOI： |
| 中文关键词: 小波包分析 特征提取 故障诊断 乏燃料剪切机 |
| 英文关键词:wavelet packet analysis feature extraction fault diagnosis spent nuclear fuel shears |
| 基金项目:国防基础科研计划基金资助项目(B3720110001) |
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| 中文摘要: |
| 针对乏燃料剪切机剪切声音信号特征提取的难题,利用小波包分析方法,对不同磨损状况刀具的剪切声音信号进行小波包变换,提取变换信号的各频段归一化能量特征向量,根据声音信号的能量特征向量可辨识不同状况的乏燃料剪切机剪切声音,从而实现乏燃料剪切机故障诊断.实验表明,该特征向量能有效识别刀具的正常磨损、一级磨损、二级磨损三种状况,有效解决了基于隐马尔可夫模型的故障模式识别中特征提取的问题. |
| 英文摘要: |
| Aiming at the voice signal feature extraction problem of spent nuclear fuel shears,the wavelet packet analysis method was proposed.The wavelet packet transform was used to the cutting sound signal of the different status tool,extracting the normalized energy eigenvector of each frequency band signal,the energy eigenvector can distinguish different status cutting sound signal,and then diagnose the fault of spent nuclear fuel shears.Experimental results show that it can effectively distinguish three conditions of the shears:the level of normal,the first or the second wear and tear,which effectively solved the feature extraction problem based on the Hidden Markov Model in shearing machine fault pattern recognition. |
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