Complete Ensemble Extreme-point Weighted Mode Decomposition with Adaptive Noise and its Application to Fault Diagnosis of Low Speed Rolling Bearings
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摘要: 极值加权模态分解(Extreme-point weighted mode decomposition,EWMD)是在经验模态分解(Empirical mode decomposition,EMD)的基础上提出的一种自适应信号分解方法,能够改善EMD的分解能力。针对于EWMD的模态混叠问题,借鉴噪声辅助分解思想,提出了自适应噪声极值加权模态分解(Complete ensemble extreme-point weighted mode decomposition with adaptive noise,CEEWMDAN)。CEEWMDAN通过向原始信号中添加辅助噪声,用改进的均值曲线构造方式提取内禀模态函数,有效地抑制模态混叠,且分解结果对集成次数的依赖性小,在保证分解精度的前提下减少了计算量。通过仿真实验数据分析验证了CEEWMDAN在提高分解性能和抑制模态混叠方面的有效性。提出了一种基于CEEWMDAN和快速谱峭度的低速滚动轴承故障诊断方法,并应用于实测数据分析。结果表明所提出的方法能够有效地识别低速滚动轴承故障。Abstract: Extreme-point weighted mode decomposition (EWMD) is an adaptive signal processing method based on empirical mode decomposition (EMD). Aiming at mode mixing problem of EWMD, the complete ensemble extreme-point weighted mode decomposition with adaptive noise (CEEWMDAN) is proposed in this paper. CEEWMDAN extracts intrinsic mode function by adding the assisted noise to original signal and using the improved mean curve, which effectively restrains the mode mixing and reduces the computational complexity. Its effectiveness is verified by simulation experimental data analysis. Finally, a fault diagnosis method for low-speed rolling bearings based on CEEWMDAN and fast spectral kurtosis is proposed and applied to the measured data analysis. The results show that the proposed fault diagnosis method can effectively identify low-speed rolling bearing faults.
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Key words:
- EMD /
- EWMD /
- mode mixing /
- rolling bearing /
- fault diagnosis /
- low speed
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表 1 IMF的能量比ER和正交性指标IO
指标 CEEMDAN CEEWMDAN EWMD ER1 0.449 5 0.386 8 10.098 4 ER2 0.549 2 0.031 3 1.185 1 ER3 0.023 2 0.032 1 0.561 5 IO 0.144 9 0.094 2 44.759 5 -
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