Peng et al., 2023 - Google Patents
Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditionsPeng et al., 2023
- Document ID
- 2628791195233692967
- Author
- Peng D
- Zhu X
- Teng W
- Liu Y
- Publication year
- Publication venue
- Mechanical Systems and Signal Processing
External Links
Snippet
Blind deconvolution (BD) methods can extract fault signatures from noisy observations. Among all the BD methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is an effective method for extracting weak periodic impulses related to bearing …
Similar Documents
Publication | Publication Date | Title |
---|---|---|
He et al. | Sparsity-based algorithm for detecting faults in rotating machines | |
Peng et al. | Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditions | |
Ming et al. | Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum | |
Jiang et al. | An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing | |
Zheng et al. | Sparse elitist group lasso denoising in frequency domain for bearing fault diagnosis | |
Cong et al. | Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis | |
Yu et al. | Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising | |
Guo et al. | Envelope order tracking for fault detection in rolling element bearings | |
Ma et al. | Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform | |
Yang et al. | Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion | |
Yan et al. | Bearing fault diagnosis via a parameter-optimized feature mode decomposition | |
Cong et al. | Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis | |
Sun et al. | Fault detection of rolling bearing using sparse representation-based adjacent signal difference | |
CN101561314A (en) | Method for testing stochastic resonance-chaotic weak signal | |
Liang et al. | An information-based K-singular-value decomposition method for rolling element bearing diagnosis | |
Li et al. | A sparsity-enhanced periodic OGS model for weak feature extraction of rolling bearing faults | |
Zheng et al. | Faults diagnosis of rolling bearings based on shift invariant K-singular value decomposition with sensitive atom nonlocal means enhancement | |
Kang et al. | Research on extracting weak repetitive transients of fault rolling element bearing | |
Zhou et al. | Sparse dictionary analysis via structure frequency response spectrum model for weak bearing fault diagnosis | |
Ma et al. | An Improved Time‐Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing | |
Chen et al. | Rolling bearing fault feature extraction method using adaptive maximum cyclostationarity blind deconvolution | |
Zhou et al. | Impulses recovery technique based on high oscillation region detection and shifted rank-1 reconstruction—Its application to bearing fault detection | |
Miao et al. | Application of improved reweighted singular value decomposition for gearbox fault diagnosis based on built-in encoder information | |
Li et al. | An enhanced K-SVD denoising algorithm based on adaptive soft-threshold shrinkage for fault detection of wind turbine rolling bearing | |
Sun et al. | Application of a novel improved adaptive CYCBD method in gearbox compound fault diagnosis |