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Fault Feature Extraction of Rolling Bearings Based on Ordered Singular Spectrum Decomposition-Multipoint Optimal
Longlong Li1, Wenhao Chen2, Wenhui Li2
1School of Mechanical Engineering, Shaanxi Polytechnic University, Xianyang 712000, China.
Entropy (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a new method using Ordered Singular Spectrum Decomposition (OSSD) and Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) to detect weak early fault signals in rolling bearings, even with significant noise.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Condition Monitoring
Background:
- Early fault detection in rolling bearings is crucial for preventing catastrophic failures.
- Weak fault-induced impact signals are difficult to extract from noisy vibration data.
Purpose of the Study:
- To develop a novel method for extracting weak fault impact signal features from rolling bearing vibration data.
- To enhance the recognizability of early fault features in the presence of strong noise.
Main Methods:
- Utilized Ordered Singular Spectrum Decomposition (OSSD) for signal decomposition and adaptive component selection.
- Employed Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) to enhance periodic impact features.
- Implemented a waveform extension strategy to maintain signal integrity after MOMEDA processing.
Main Results:
- The proposed OSSD-MOMEDA method demonstrated robust noise resistance.
- Effectively extracted early fault features of rolling bearings under strong noise conditions.
- Simulation and experimental results validated the method's accuracy and effectiveness.
Conclusions:
- The combined OSSD-MOMEDA approach provides an effective solution for early fault detection in rolling bearings.
- The method overcomes challenges posed by weak signals and high noise levels in vibration analysis.
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