Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency
Chuan Li1, Vinicio Sanchez2, Grover Zurita2
1Research Center of System Health Maintenance, Chongqing Technology and Business University, Chongqing 400067, China; Department of Mechanical Engineering, Universidad Politécnica Salesiana, Cuenca, Ecuador.
ISA Transactions
|November 7, 2015
Summary
This study introduces a new method for detecting bearing defects using generalized synchrosqueezing transform (GST) and time-frequency (TF) ridge extraction. The technique effectively identifies defective characteristic frequencies and rotation frequencies in machinery, ensuring safer operation.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Condition Monitoring
Background:
- Healthy rolling element bearings are critical for safe rotating machinery operation.
- Time-frequency (TF) analysis is effective for detecting bearing defects, especially under varying speeds.
- Simultaneous identification of defective characteristic frequency and rotation frequency without a tachometer presents a significant challenge.
Purpose of the Study:
- To propose a novel technique for bearing defect detection using generalized synchrosqueezing transform (GST) guided by enhanced TF ridge extraction.
- To address the challenge of simultaneously identifying defective characteristic frequency and rotation frequency in the absence of a tachometer.
- To validate the effectiveness of the proposed technique using both simulated and experimental bearing vibration data.
Main Methods:
- Extraction of low frequency and resonance bands from bearing vibration measurements.
- Time-frequency (TF) representation using short-time Fourier transform (STFT) for band components and envelopes.
- Enhanced TF ridge extraction via harmonic summation search and ridge candidate fusion.
- Guidance of generalized synchrosqueezing transform (GST) using the inverse of extracted TF ridges for spectral sharpening.
- Identification of rotation frequency and defective characteristic frequency in rectified and synchrosqueezed TF spectra.
Main Results:
- The proposed technique successfully maps chirped TF representations to constant ones, sharpening the spectra.
- Both rotation frequency and defective characteristic frequency were clearly identifiable in the processed TF spectra.
- Validation using simulated and experimental signals confirmed the technique's effectiveness in bearing defect detection.
Conclusions:
- The developed technique, combining enhanced TF ridge extraction with GST, provides an effective solution for bearing defect detection.
- This method enables reliable identification of key frequencies crucial for diagnosing bearing health, even without tachometer data.
- The findings contribute to improved condition monitoring and predictive maintenance strategies for rotating machinery.
Keywords:
Defect detectionGeneralized synchrosqueezing transformRolling element bearingShort-time Fourier transformTime–frequency ridgeMore Related Videos
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