电子测量与仪器学报

郑近德,苏缪涎,潘海洋,童靳于,潘紫微.匀相窄波局部特征尺度分解方法及其在机械故障诊断中的应用[J].电子测量与仪器学报,2021,35(2):50-58

匀相窄波局部特征尺度分解方法及其在机械故障诊断中的应用

Uniform phase local characteristic scale decomposition and its applications in mechanical fault diagnosis

DOI：

英文关键词:local characteristic scale decomposition mode mixing noise assisted analysis rotating machinery fault diagnosis

基金项目:国家自然科学基金（51975004）、安徽省高校自然科学研究重点项目（KJ20190053）和安徽理工大学矿山智能装备与技术安徽省重点实验室开放基金项目(201902005)资助

作者	单位
郑近德	1安徽工业大学机械工程学院马鞍山243032； 2安徽理工大学矿山智能装备与技术安徽省重点实验室淮南232001
苏缪涎	1安徽工业大学机械工程学院马鞍山243032；
潘海洋	1安徽工业大学机械工程学院马鞍山243032；
童靳于	1安徽工业大学机械工程学院马鞍山243032；
潘紫微	1安徽工业大学机械工程学院马鞍山243032；

Author	Institution
Zheng Jinde	1School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China; 2Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science & Technology, Huainan 232001, China
Su Miaoxian	1School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China;
Pan Haiyang	1School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China;
Tong Jinyu	1School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China;
Pan Ziwei	1School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China;

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中文摘要:

局部特征尺度分解（local characteristic scale decomposition, LCD）方法在改善了经验模态分解（empirical mode decomposition, EMD）方法的同时，也继承了EMD的模态混叠问题。噪声辅助分解是解决EMD模态混叠问题的主要方法之一，但由于LCD对于噪声更加敏感，如果采用总体平均经验模态分解（ensemble empirical mode decomposition， EEMD）方法中的白噪声作为辅助信号不仅不能够有效地改善LCD中的模态混叠问题，还会产生较多的虚假分量。对此，提出一种改进的LCD方法——匀相窄波局部特征尺度分解方法（uniform phase local characteristic scale decomposition, UPLCD）。UPLCD采用具有均变相位的窄波信号来代替白噪声作为辅助分解信号，能够在抑制LCD模态混叠的同时，避免白噪声带来虚假分量增多的情况。通过仿真信号分析，验证了UPLCD方法抑制模态混叠的有效性。并将所提出的方法应用到机械故障诊断中，和EEMD、LCD和匀变相位经验模态分解（uniform phase empirical mode decomposition， UPEMD）等方法对比，结果表明，所提出的UPLCD方法能够有效地处理旋转机械故障模态信号，在分解精度和抑制干扰信号等方面更具优势。

英文摘要:

Local characteristic scale decomposition (LCD) has improved the empirical mode decomposition (EMD) method, but it also inherits the mode mixing of EMD. Noise assisted analysis is one of the important methods to solve the mode mixing, however, LCD is more sensitive to noise. If we adopt the white noise used in ensemble empirical mode decomposition (EEMD) as an auxiliary signal, the mode mixing cannot be effectively resolved and more false components will produce. An improved LCD method termed uniform phase local characteristic scale decomposition (UPLCD) is proposed to solve the above problem. UPLCD uses narrow wave signal with uniform phase as auxiliary signal, which can suppress the mode mixing and avoid the increase of false components caused by white noise. After the validity of UPLCD is verified by the simulation signal analysis, the proposed method is applied to the mechanical fault diagnosis by comparing it with EEMD, LCD and uniform phase empirical mode (UPEMD). The results show that the proposed UPLCD can effectively decompose the modes of rotating machinery signals and has more advantages in decomposition accuracy and interference suppression than the above three methods.

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