Zheng Jinde, Pan Haiyang
School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243032, China; School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney NSW 2052, Australia.
School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243032, China.
ISA Trans. 2020 Nov;106:392-401. doi: 10.1016/j.isatra.2020.06.011. Epub 2020 Jun 18.
As an effective signal separation method of non-stationary signal, empirical mode decomposition (EMD) has been widely used in the data or time series analysis of many engineering fields. However, the decomposing result of EMD often is affected by the fitting in mean curve construction and the sifting process. In this paper, the mean-optimized mode decomposition (MOMD) procedure is proposed to enhance the performance of the original EMD in mean curve construction. Also, the proposed MOMD algorithm is compared with original EMD through analyzing two artificial signals and the analysis results demonstrate that MOMD has much more significantly improvement in decomposition performance and precision than the original EMD. Last, MOMD is introduced to the signal processing stemming from the faulty rolling bearing and the rotor system with failure. Also, the comparison of the proposed MOMD method with EMD was made and the analysis results show that MOMD obtains much more accurate IMFs and fault diagnostic effect than the original EMD method.
作为一种有效的非平稳信号分离方法，经验模态分解（EMD）已广泛应用于许多工程领域的数据或时间序列分析中。然而，EMD的分解结果常常受到均值曲线构建和筛选过程中的拟合影响。本文提出了均值优化模态分解（MOMD）方法，以提高原始EMD在均值曲线构建方面的性能。此外，通过分析两个人造信号，将所提出的MOMD算法与原始EMD进行了比较，分析结果表明，MOMD在分解性能和精度方面比原始EMD有更显著的提高。最后，将MOMD引入到来自故障滚动轴承和故障转子系统的信号处理中。此外，将所提出的MOMD方法与EMD进行了比较，分析结果表明，MOMD比原始EMD方法能获得更准确的固有模态函数（IMF）和故障诊断效果。
