...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert systems with applications >A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults
【24h】

A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults

机译：一种杂交微调VMD和CNN方案，用于旋转机械的未培训复合故障诊断，具有不平等严重的故障

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

In the case of a compound fault diagnosis of rotating machinery, when two failures with unequal severity occur in distinct parts of the system, the detection of a minor fault is a complicated and challenging task. In this case, the minor fault is overshadowed by the more severe one, and the characteristics of the compound fault are prone to the more severe one. Generally, the proposed methods in the literature consider compound failure as an individual fault type and unrelated to the corresponding single faults, either at the different locations of a sensitive component or in two separate parts, such as the bearing and gear, with approximately the same fault severity. Considering these issues, this study proposes a novel end-to-end fault diagnosis method based on fine-tuned VMD and convolutional neural network (CNN). The main idea is that CNN is trained only on a healthy and single fault dataset, without the use of compound fault data in training. In the test stage of the CNN model, the intelligent method alarms an untrained compound fault state if acquired probabilities of CNN output satisfy a set of probabilistic conditions. The performance of the fine-tuned VMD and the proposed hybrid method is evaluated by the decomposition of a simulated vibration signal and the analysis of a gearbox system with a compound fault scenario in such a way that one fault is minor and the other severe. The results obtained show the high accuracy of the proposed method in compound fault diagnosis and the feature extraction and classification of a minor fault in the presence of a more severe one.
机译：在旋转机械的复合故障诊断的情况下，当系统不同的部分发生不等的严重性的两个故障时，次要故障的检测是一个复杂和具有挑战性的任务。在这种情况下，轻微的故障被更严重的较重，复合故障的特性易于更严重。通常，文献中所提出的方法将复合失败作为单独的故障类型和相应的单个故障无关，在敏感部件的不同位置或两个单独的部件（例如轴承和齿轮）中，具有大致相同的故障严重性。考虑到这些问题，本研究提出了一种基于微调VMD和卷积神经网络（CNN）的新型端到端故障诊断方法。主要思想是CNN仅在健康和单个故障数据集上培训，而无需使用复合故障数据在训练中。在CNN模型的测试阶段，如果CNN输出的获取概率满足一组概率条件，则智能方法报警未经培训的复合故障状态。通过模拟振动信号的分解和具有复合故障场景的齿轮箱系统的分解来评估微调VMD和所提出的混合方法的性能，以一种故障是次要的一种故障。得到的结果表明，在复合故障诊断中提出的方法的高精度以及在存在更严重的情况下的轻微故障的特征提取和分类。

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