• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >A time series model-based method for gear tooth crack detection and severity assessment under random speed variation
【24h】

A time series model-based method for gear tooth crack detection and severity assessment under random speed variation

机译：基于时间序列模型的随机速度变化下的齿轮齿裂纹检测和严重程度评估方法

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

In industry (e.g., wind power), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults and assess their severity using vibration signals collected under different speed profiles. A few studies have been reported for condition monitoring of gearboxes under random speed variations, including a novelty diagnostic method and a support vector machine (SVM) based method. However, these methods either are based on the strict assumption that the rotating speed does not vary significantly within a rotating cycle or have the drawback of low classification accuracy. This paper presents a time series model-based method for gear tooth crack detection and severity assessment under random speed variation. Specifically, the rotating speed and phase are considered as covariates in a linear parameter varying autoregression (AR) model for representing impulsive vibration signals. We propose refined B-splines for map-ping the dependency between AR coefficients and the rotating phase. The performance of the presented time series model-based method has been validated using laboratory signals. The presented method can assess 93.8% of the tooth crack severity state correctly, which is better than the novelty diagnostic method (74.4%) and SVM-based method (87.7%).
机译：在工业（例如，风电）中，变速箱经常在随机速度变化下运行。期望一种情况监测系统检测故障并使用在不同速度配置文件下收集的振动信号进行评估它们的严重性。据报道，随机速度变化下的齿轮箱的条件监测有一些研究，包括一种新颖的诊断方法和基于支持向量机（SVM）的方法。然而，这些方法是基于严格的假设，即旋转速度在旋转循环内不会显着变化，或者具有低分类精度的缺点。本文提出了一种基于时间序列模型的齿轮齿裂纹检测和随机速度变化的严重性评估方法。具体地，旋转速度和相位被认为是用于表示脉冲振动信号的线性参数变化自回归（AR）模型中的协调因子。我们提出了用于映射AR系数和旋转阶段之间的依赖性的精制B样条。使用实验室信号验证了所提出的时间序列模型的方法的性能。呈现的方法可以正确评估93.8％的牙齿裂纹严重程度状态，比新颖性诊断方法（74.4％）和基于SVM的方法（87.7％）更好。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2021年第7期|107605.1-107605.20|共20页
  • 作者

    Yuejian Chen; Stephan Schmidt; P. Stephan Heyns; Ming J. Zuo;

  • 作者单位

    Department of Mechanical Engineering University of Alberta Edmonton Alberta T6G 1H9 Canada;

    Centre for Asset Integrity Management Department of Mechanical and Aeronautical Engineering University of Pretoria Pretoria South Africa;

    Centre for Asset Integrity Management Department of Mechanical and Aeronautical Engineering University of Pretoria Pretoria South Africa;

    Department of Mechanical Engineering University of Alberta Edmonton Alberta T6G 1H9 Canada;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Gearbox; Condition monitoring; Random speed variation; Time series model;

    机译：变速箱;状态监测;随机速度变化;时间序列模型;

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