• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with Optimal Decomposition Depth
【2h】

Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with Optimal Decomposition Depth

机译：使用具有最佳分解深度的最大重叠离散小波变换来增强油屑传感器的磨损颗粒监测能力

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Oil debris sensors are effective tools to monitor wear particles in lubricants. For in situ applications, surrounding noise and vibration interferences often distort the oil debris signature of the sensor. Hence extracting oil debris signatures from sensor signals is a challenging task for wear particle monitoring. In this paper we employ the maximal overlap discrete wavelet transform (MODWT) with optimal decomposition depth to enhance the wear particle monitoring capability. The sensor signal is decomposed by the MODWT into different depths for detecting the wear particle existence. To extract the authentic particle signature with minimal distortion, the root mean square deviation of kurtosis value of the segmented signal residue is adopted as a criterion to obtain the optimal decomposition depth for the MODWT. The proposed approach is evaluated using both simulated and experimental wear particles. The results show that the present method can improve the oil debris monitoring capability without structural upgrade requirements.
机译：油屑传感器是监测润滑剂中磨损颗粒的有效工具。对于现场应用，周围的噪音和振动干扰通常会使传感器的油屑痕迹失真。因此，从传感器信号中提取油屑信号对于磨损颗粒的监测是一项艰巨的任务。在本文中，我们采用具有最佳分解深度的最大重叠离散小波变换（MODWT）来增强磨损颗粒监测能力。 MODWT将传感器信号分解为不同的深度，以检测磨损颗粒的存在。为了提取具有最小失真的真实粒子特征，采用分段信号残差的峰度值的均方根偏差作为准则，以获得MODWT的最佳分解深度。拟议的方法是使用模拟和实验磨损颗粒进行评估的。结果表明，该方法无需结构升级即可提高油屑监测能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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