Enhancement of the wear particle monitoring capability of oil debris sensors using a maximal overlap discrete wavelet transform with optimal decomposition depth.
Enhancement of the wear particle monitoring capability of oil debris sensors using a maximal overlap discrete wavelet transform with optimal decomposition depth.
复制标题
采用最佳分解深度的最大重叠离散小波变换增强油屑传感器磨损颗粒监测能力
DOI：
10.3390/s140406207
复制
发表时间：
2014-03-28
期刊：
影响因子：
--
通讯作者：
Liang M
中科院分区：
文献类型：
--
作者：
Li C;Peng J;Liang M
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，周围的噪声和振动干扰通常会扭曲传感器的油屑特征。因此，从传感器信号中提取油屑特征对于磨损颗粒监测来说是一项具有挑战性的任务。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深度，以检测磨损颗粒的存在。为了以最小的失真提取真实的粒子特征，采用分段信号残差峰度值的均方根偏差作为标准来获得MODWT的最佳分解深度。使用模拟和实验磨损颗粒对所提出的方法进行评估。结果表明，该方法无需结构升级即可提高油屑监测能力。
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.
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