Abstract
As an effective tool for nonlinear and non-stationary signal separation method, empirical mode decomposition (EMD) has attracted a lot of attention of many scholars and has been successfully applied to many engineering areas. Since the kernel of EMD is to define a baseline and then implement the sift process, it is important to select an appropriate baseline to improve the performance of EMD for an accurate decomposition. However, it is difficult to choose the most suitable method for dealing with a given arbitrary signal. Besides, the baseline constructed through the existing methods generally does not equal the expected one. To address these problems, we propose a new generalized framework for adaptive mode decomposition (GF-AMD) based on EMD, in which the optimal baseline is firstly chosen from different ones and a weighted factor is introduced to adjust the baseline for getting the optimal one. Then the optimal baseline is implemented to the sift process of EMD. Two simulation signals are used to verify the effectiveness and superiority to EMD. Finally, the proposed GF-AMD method is applied to the vibration signal analysis of faulty rotary machinery by comparing with EMD method and the analysis results indicate its effect and superiority.
Highlights
Empirical mode decomposition (EMD) has been proved to be an effective and powerful time-frequency analysis tool [1]–[3], by using which a complicated nonlinear or non-stationary signal can be adaptively decomposed into a series of intrinsic mode functions (IMFs) without priori determined basis functions
We use the index of orthogonality (IO) [1] to do the same thing with lots of trials and we found that σ has the same change trend with IO, i.e. the IO and the σ both reach the minimum point for the optimal baseline
The superiority of generalized framework for adaptive mode decomposition (GF-AMD) to EMD, VMD and local characteristicscale decomposition (LCD) is verified by analyzing two simulation signals and the result indicates that the IMFs obtained by GF-AMD get better effect in accuracy and orthogonality
Summary
School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, China This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0805100, in part by the National Natural Science Foundation of China under Grant 51975004, in part by the Key Program of Natural Science Research of Higher Education in Anhui Province of China under Grant KJ2019A0053, and in part by the Engineering Research Center of Ministry of Education for Hydraulic Vibration and Control under Grant HVC201904.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
