Research on rolling bearing fault diagnosis technology based on singular value decomposition
Source abstract
Abstract
To solve the difficulty of selecting the number of effective singular values in Singular Value Decomposition denoising, a new method to determine the number of effective singular values is proposed. The proposed method to determine the number of effective singular values is based on the non-zero singular value distribution law of the Hankel matrix constructed by the signal. Specifically, the number of effective singular values in the Hankel matrix is twice the number of frequencies contained in the signal, and the difference between the effective singular values of the noisy signal and the non-zero singular values of the pure signal is very small. The proposed method for determining the number of effective singular values is to perform differential processing on the singular values of the signal and normalize the difference obtained. An empirical parameter T is provided, and the number of effective singular values is determined by comparing them with the normalized results. The proposed method is applied to the simulated and measured rolling bearing signals, and the results are compared with the wavelet threshold denoising method. The results show that the proposed method for determining the number of singular values can effectively filter out the noise frequency contained in the signal while maintaining the characteristic frequency of the signal and achieving the purpose of mechanical equipment fault diagnosis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Engineering Diagnostics and Reliability · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automotive Engineering, Hubei University of Automotive Technology 1 , Shiyan, Hubei Province, China
2 Key Laboratory of Automotive Power Train and Electronics (Hubei University of Automotive Technology), Shiyan, Hubei Province, China
3 School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou , Henan Province, China
Funding
Key Laboratory of Automotive Power Train and Electronic · China Postdoctoral Science Foundation
References (22)
Show source-provided references
1. Fault feature signal extraction method for rolling bearings in gas turbine engines based on threshold parameter decision screening10.1016/j.measurement.2024.114567 ↗
2. A multi-scale attention mechanism based domain adversarial neural network strategy for bearing fault diagnosis10.3390/act12050188 ↗
3. CMAFI—Copula-based multifeature autocorrelation fault identification of rolling bearing10.1016/j.ymssp.2024.111221 ↗
4. Early rolling bearing fault diagnosis in induction motors based on on-rotor sensing vibrations10.1016/j.measurement.2023.113614 ↗
5. Rolling bearing weak fault feature extraction under variable speed conditions via joint sparsity and low-rankness in the cyclic order-frequency domain10.3390/app12052449 ↗
6. An intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network10.3390/s120505919 ↗
7. A novel strategy for signal denoising using two-layer SVD and its application to rub-impact fault diagnosis of aeroengine10.3390/app12052342 ↗
8. Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis10.1016/j.isatra.2024.01.027 ↗
9. A novel rolling bearing fault diagnosis method based on adaptive denoising convolutional neural network under noise background10.1016/j.measurement.2023.113209 ↗
10. Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults10.1016/j.ymssp.2024.111142 ↗
11. Improvement to blind image denoising by using local pixel grouping with SVD10.1016/j.procs.2016.03.041 ↗
12. A simplified and accelerated implementation of SVD for filtering ultrafast power Doppler images10.1016/j.ultras.2023.107099 ↗
13. Spurious signals attenuation using SVD-based Wiener filter for near-surface ambient noise surface wave imaging10.1016/j.jappgeo.2020.104220 ↗
14. Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline10.1016/j.ymssp.2015.10.034 ↗
15. Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis10.1016/j.ymssp.2008.09.009 ↗
16. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock10.1016/j.ymssp.2011.01.003 ↗
17. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults10.1016/j.ymssp.2015.08.012 ↗
18. A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery10.1016/j.ymssp.2017.02.036 ↗
19. An EEMD-SVD-LWT algorithm for denoising a lidar signal10.1016/j.measurement.2020.108405 ↗
20. A combined noise reduction method for floodgate vibration signals based on adaptive singular value decomposition and improved complete ensemble empirical mode decomposition with adaptive noise10.3390/w15244287 ↗
21. Selection of the number of effective singular values for noise reduction10.1016/j.ymssp.2023.110175 ↗
22. A novel joint denoising method for gear fault diagnosis with improved quaternion singular value decomposition10.1016/j.measurement.2024.114165 ↗