Fault Diagnosis of Rolling Bearing Using Improved Wavelet Threshold Denoising and Fast Spectral Correlation Analysis
Rolling bearings are important parts of mechanical equipment. However, the early failures of the bearing are usually masked by heavy noise. This brings about difficulties to the extraction of its fault features. Therefore, there is a need to develop a reliable method for early fault detection of the...
Saved in:
| Main Authors: | Shaoning Tian, Dong Zhen, Junchao Guo, Haiyang Li, Hao Zhang, Fengshou Gu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5510879 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rolling Based on Multi-Source Time–Frequency Feature Fusion with a Wavelet-Convolution, Channel-Attention-Residual Network-Bearing Fault Diagnosis Method
by: Tongshuhao Feng, et al.
Published: (2025-06-01) -
Resilient fault detection for industrial process using adaptive Fisher discriminant analysis with wavelet denoising
by: Faizan E. Mustafa, et al.
Published: (2025-09-01) -
Advanced gear fault diagnosis in non-stationary conditions with an improved CEEMDAN-wavelet denoising technique
by: Zakarya Ouelaa, et al.
Published: (2025-07-01) -
Fault Diagnosis of Rolling Bearing Based on Bayesian Network and Fuzzy Evaluation
by: MA De-zhong, et al.
Published: (2018-10-01) -
Induction Machine Bearing Fault Detection Using Empirical Wavelet Transform
by: Ricardo Lopez-Gutierrez, et al.
Published: (2022-01-01)