采用HHT算法与卷积神经网络诊断轴承复合故障
TH165%TH17; 针对农业机械装备中滚动轴承复合故障特征提取与智能诊断问题,该文提出了一种将希尔伯特-黄变换的改进算法(improved hilbert-huang transform,IHHT)与卷积神经网络(convolution neural network,CNN)相结合的诊断方法.首先,通过多种群差分进化改进的集合经验模式分解(multiple population differential evolution-ensemble empirical mode decomposition,MPDE-EEMD)和敏感固有模态函数筛选方法来改进HHT,提取出故障信号时频特征.然后,在...
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| Published in | 农业工程学报 Vol. 36; no. 4; pp. 34 - 43 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
昆明理工大学机电工程学院,昆明 650500
15.02.2020
云南农业大学机电工程学院,昆明 650201%昆明理工大学机电工程学院,昆明,650500 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2020.04.005 |
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| Abstract | TH165%TH17; 针对农业机械装备中滚动轴承复合故障特征提取与智能诊断问题,该文提出了一种将希尔伯特-黄变换的改进算法(improved hilbert-huang transform,IHHT)与卷积神经网络(convolution neural network,CNN)相结合的诊断方法.首先,通过多种群差分进化改进的集合经验模式分解(multiple population differential evolution-ensemble empirical mode decomposition,MPDE-EEMD)和敏感固有模态函数筛选方法来改进HHT,提取出故障信号时频特征.然后,在AlexNet网络模型基础上遍历所有可能的CNN模型组合,构建出适应于滚动轴承故障诊断的CNN网络模型.再将训练集生成的IHHT时频图输入CNN中进行学习,不断更新网络参数;并将该模型应用于测试集,输出故障识别结果.最后,通过滚动轴承单一故障和复合故障2种试验,将所提出的IHHT+CNN方法分别与传统的BP神经网络、DWT+CNN和STFT+CNN方法进行比较.研究表明,该文的IHHT+CNN方法对单一与复合故障的正确率分别达到100％和99.74％,均高于其他3种方法,实现了不同工况下端到端的轴承复合故障智能诊断,并具有较好的泛化能力和鲁棒性. |
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| AbstractList | TH165%TH17; 针对农业机械装备中滚动轴承复合故障特征提取与智能诊断问题,该文提出了一种将希尔伯特-黄变换的改进算法(improved hilbert-huang transform,IHHT)与卷积神经网络(convolution neural network,CNN)相结合的诊断方法.首先,通过多种群差分进化改进的集合经验模式分解(multiple population differential evolution-ensemble empirical mode decomposition,MPDE-EEMD)和敏感固有模态函数筛选方法来改进HHT,提取出故障信号时频特征.然后,在AlexNet网络模型基础上遍历所有可能的CNN模型组合,构建出适应于滚动轴承故障诊断的CNN网络模型.再将训练集生成的IHHT时频图输入CNN中进行学习,不断更新网络参数;并将该模型应用于测试集,输出故障识别结果.最后,通过滚动轴承单一故障和复合故障2种试验,将所提出的IHHT+CNN方法分别与传统的BP神经网络、DWT+CNN和STFT+CNN方法进行比较.研究表明,该文的IHHT+CNN方法对单一与复合故障的正确率分别达到100％和99.74％,均高于其他3种方法,实现了不同工况下端到端的轴承复合故障智能诊断,并具有较好的泛化能力和鲁棒性. |
| Author | 刘韬 伍星 施杰 |
| AuthorAffiliation | 昆明理工大学机电工程学院,昆明 650500;云南农业大学机电工程学院,昆明 650201%昆明理工大学机电工程学院,昆明,650500 |
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| Author_FL | Wu Xing Liu Tao Shi Jie |
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| DocumentTitle_FL | Bearing compound fault diagnosis based on HHT algorithm and convolution neural network |
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| Keywords | 轴承 卷积神经网络 希尔伯特-黄变换 多种群差分进化 集合经验模式分解 故障诊断 |
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| Publisher | 昆明理工大学机电工程学院,昆明 650500 云南农业大学机电工程学院,昆明 650201%昆明理工大学机电工程学院,昆明,650500 |
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| Title | 采用HHT算法与卷积神经网络诊断轴承复合故障 |
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