...
首页> 外文期刊>Mechanical systems and signal processing >Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
【24h】

Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

机译:基于深度学习的层次诊断网络的构建及其在滚动轴承故障模式识别中的应用

获取原文
获取原文并翻译 | 示例
           

摘要

A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two-stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.
机译:提出了一种新的层次诊断网络(HDN),它通过逐层收集深度信任网络(DBN)来对机械系统进行层次识别。 HDN的较深层对从最后一层生成的结果进行了更详细的分类,以提供用于不同任务的代表性功能。两层HDN用于具有小波包能量特征的两阶段诊断。第一层旨在识别故障类型,而第二层旨在从第一层的结果进一步识别故障严重性等级。为了确认HDN的有效性,采用了由支持向量机和反向传播神经元网络(BPNN)构建的两个相似网络进行全面比较。实验结果表明,HDN在精确的多阶段诊断中具有很高的可靠性,并且可以克服由噪声和其他干扰引起的重叠问题。

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