...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Mechanical Science and Technology >A new fault diagnosis method based on convolutional neural network and compressive sensing
【24h】

A new fault diagnosis method based on convolutional neural network and compressive sensing

机译：一种基于卷积神经网络和压缩感测的新故障诊断方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Compressive sensing is an efficient machinery monitoring framework, which just needs to sample and store a small amount of observed signal. However, traditional reconstruction and fault detection methods cost great time and the accuracy is not satisfied. For this problem, a 1D convolutional neural network (CNN) is adopted here for fault diagnosis using the compressed signal. CNN replaces the reconstruction and fault detection processes and greatly improves the performance. Since the main information has been reserved in the compressed signal, the CNN is able to extract features from it automatically. The experiments on compressed gearbox signal demonstrated that CNN not only achieves better accuracy but also costs less time. The influencing factors of CNN have been discussed, and we compared the CNN with other classifiers. Moreover, the CNN model was also tested on bearing dataset from Case Western Reserve University. The proposed model achieves more than 90 % accuracy even for 50 % compressed signal.
机译：压缩传感是一种有效的机械监测框架，只需要采样和存储少量观察信号。但是，传统的重建和故障检测方法花了很多时间，并且不满足精度。对于这个问题，这里采用1D卷积神经网络（CNN）用于使用压缩信号进行故障诊断。 CNN取代了重建和故障检测过程，大大提高了性能。由于主信息已被保留在压缩信号中，因此CNN能够自动从其中提取特征。压缩齿轮箱信号的实验表明，CNN不仅可以实现更好的准确性，而且还花费较少的时间。已经讨论了CNN的影响因素，并将CNN与其他分类器进行了比较。此外，CNN模型也在壳体西部储备大学的轴承数据集上进行了测试。即使对于50％压缩信号，所提出的模型即使为50％的压缩信号达到90％以上。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号