互补集合自适应最稀疏窄带分解及其应用

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (20) : 31-37.

论文

互补集合自适应最稀疏窄带分解及其应用

陈君航1,2，彭延峰1,2，李学军1,2，韩清铠3，李鸿光1,4

作者信息 +

Complementary ensemble adaptive sparsest narrow-band decomposition and its application

CHEN Junhang1,2,PENG Yanfeng1,2,LI Xuejun1,2,HAN Qingkai3,LI Hongguang1,4

Author information +

文章历史 +

摘要

自适应最稀疏窄带分解(Adaptive Sparsest Narrow-band Decomposition，ASNBD)是在包含内禀模态函数(Intrinsic Mode Functions, IMF )的过完备字典库中搜索信号的最稀疏解，将信号分解转化为优化问题, 但在强噪声干扰时计算精度仍有待提高。因此在结合了互补集合经验模态分解(Complementary Ensemble Empirical Mode Decomposition，CEEMD)算法，得到了新的互补集合自适应最稀疏窄带分解(Complementary Ensemble Adaptive Sparsest Narrow-band Decomposition，CE-ASNBD)方法。此方法是加入成对符号相反的白噪声到目标信号，从而减小重构误差，在对滤波器参数的优化过程中实现信号的自适应分解。对仿真和实验数据的分析结果表明，该方法在抑制模态混淆、端点效应、性能、提高分量的正交性和准确性等方面要优于CEEMD和ASNBD方法，并能有效应用于滚动轴承故障诊断。

Abstract

Adaptive sparsest narrow-band decomposition (ASNBD) is the most sparse solution for searching signals in the over-complete dictionary library containing intrinsic mode functions (IMF), which transforms the signal Decomposition into an optimization problem, but the calculation accuracy still needs to be improved in the case of strong noise interference.Therefore, in combination with the algorithm of thecomplementary ensemble empirical mode decomposition (CEEMD), a new method of the complementary ensemble adaptive sparsest narrow-band decomposition (CE-ASNBD) was obtained.In this method, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters.The analysis results of simulation and experimental data show that this method is superior to CEEMD and ASNBD in inhibiting mode confusion, endpoint effect, performance, improving component orthogonality and accuracy, and can be effectively used in fault diagnosis of rolling bearing.

导出引用

陈君航1,2，彭延峰1,2，李学军1,2，韩清铠3，李鸿光1,4. 互补集合自适应最稀疏窄带分解及其应用[J]. 振动与冲击, 2019, 38(20): 31-37

CHEN Junhang1,2,PENG Yanfeng1,2,LI Xuejun1,2,HAN Qingkai3,LI Hongguang1,4. Complementary ensemble adaptive sparsest narrow-band decomposition and its application[J]. Journal of Vibration and Shock, 2019, 38(20): 31-37

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