碎片
签名(拓扑)
信号(编程语言)
噪音(视频)
分解
计算机科学
分解法(排队论)
辛几何
模式(计算机接口)
人工智能
算法
声学
地质学
数学
几何学
物理
图像(数学)
离散数学
操作系统
海洋学
生物
程序设计语言
生态学
作者
Bing Yu,Ce‐Wen Nan,Tianhong Zhang
出处
期刊:Measurement
[Elsevier]
日期:2021-11-01
卷期号:185: 110056-110056
被引量:19
标识
DOI:10.1016/j.measurement.2021.110056
摘要
On line lubricating oil debris measurement is an efficient way to judge the operating status of a machine, and the inductive oil debris sensor is widely adopted. Considering the debris sensor works in a harsh environment, it is a challenge work to reduce or separate the noise via signal processing, especially for the requirements of detecting small-sized wear debris. To overcome the limitations of existing methods, a novel signal decomposition method called symplectic geometry mode decomposition (SGMD) is proposed to extract the signature of oil debris. SGMD can remodel the state and eliminating noise adaptively, and the simulation results manifest that SGMD can extract the signature of debris accurately and effectively. When analyzing the experimental signal, the SGMD and EMD are combined, and the results show that it has a better decomposition ability than EMD or wavelet decomposition.
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