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Mean-optimized mode decomposition: An improved EMD approach for non-stationary signal processing.
1School 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.
ISA Transactions
|June 28, 2020
Summary
Mean-optimized mode decomposition (MOMD) improves upon empirical mode decomposition (EMD) for non-stationary signal analysis. MOMD enhances mean curve construction, leading to more precise signal decomposition and accurate fault diagnosis in engineering applications.
Area of Science:
- Engineering
- Signal Processing
- Data Analysis
Background:
- Empirical Mode Decomposition (EMD) is a signal separation method for non-stationary signals.
- EMD's performance can be limited by mean curve construction and sifting processes.
Purpose of the Study:
- To propose Mean-Optimized Mode Decomposition (MOMD) to enhance EMD's performance.
- To compare MOMD with EMD for signal decomposition accuracy and fault diagnosis.
Main Methods:
- Developed the Mean-Optimized Mode Decomposition (MOMD) algorithm.
- Analyzed artificial signals to compare MOMD and EMD performance.
- Applied MOMD to signal processing of faulty rolling bearings and rotor systems.
Main Results:
- MOMD demonstrated significantly improved decomposition performance and precision over EMD.
- Analysis of artificial signals confirmed MOMD's superiority.
- MOMD provided more accurate intrinsic mode functions (IMFs) and fault diagnostic effects.
Conclusions:
- MOMD offers enhanced performance in mean curve construction for signal decomposition.
- MOMD is a more accurate and effective method than EMD for analyzing non-stationary signals.
- MOMD shows promise for improved fault diagnosis in mechanical systems.
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