• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Multi-scale deep intra-class transfer learning for bearing fault diagnosis
【24h】

Multi-scale deep intra-class transfer learning for bearing fault diagnosis

机译：多尺度深度级载重诊断型转移学习

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The tremendous success of deep learning in machine fault diagnosis is dependent on the hypothesis that training and test datasets are subordinated to the same distribution. This subordination is difficult to meet in practical scenarios of industrial applications. On the one hand, the working conditions of rotating machinery can change easily. On the other hand, vibration data and labels are difficult to obtain to train a specific model for each working condition. In this study, we solve these problems by constructing a novel deep transfer learning model called multi-scale deep intra-class adaptation network, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low-level features at multiple scales and obtain high-level features as input for the classifier. Pseudo labels are then computed to shorten the conditional distribution distance of vibration data collected under different working loads for intra-class adaptation. The proposed method is validated using two datasets to recognize the bearing normal state, the inner race, the ball and outer race faults, and their fault degrees under four different working loads. The high-precision diagnosis results of 24 transfer learning experiments reveal the reliability and generalizability of the constructed model.
机译：在机器故障诊断中深入学习的巨大成功取决于训练和测试数据集的假设从属于同一分布。在工业应用的实际情况中难以满足这种从属。一方面，旋转机械的工作条件可以很容易地改变。另一方面，振动数据和标签难以获得为每个工作条件训练特定模型。在这项研究中，我们通过构建一个名为Multi-Scale Intran-Class适配网络的新型深度传输学习模型来解决这些问题，该模型首先使用修改的Reset-50来提取低级功能，然后构建多个比例特征学习者分析多个尺度的这些低级功能，并获得分类器输入的高级功能。然后计算伪标签以缩短在不同工作载荷下收集的振动数据的条件分布距离，以用于类内适配。使用两个数据集验证所提出的方法，以识别轴承正常状态，内部竞争，球和外部竞争故障，以及在四个不同的工作负载下的故障度。 24转移学习实验的高精度诊断结果揭示了构造模型的可靠性和普遍性。

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