Shao et al., 2014 - Google Patents
The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet …Shao et al., 2014
- Document ID
- 5842023092601557711
- Author
- Shao R
- Hu W
- Wang Y
- Qi X
- Publication year
- Publication venue
- Measurement
External Links
Snippet
The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth …
- 238000000513 principal component analysis 0 title abstract description 63
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shao et al. | The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform | |
Zhao et al. | A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis | |
Cheng et al. | A noise reduction method based on adaptive weighted symplectic geometry decomposition and its application in early gear fault diagnosis | |
He | Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis | |
Liu et al. | A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM | |
Li et al. | Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy | |
Li et al. | Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis | |
CN102721545B (en) | Rolling bearing failure diagnostic method based on multi-characteristic parameter | |
Ma et al. | Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform | |
Han et al. | A recursive sparse representation strategy for bearing fault diagnosis | |
Junlin et al. | A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT | |
Shi et al. | Intelligent bearing fault signature extraction via iterative oscillatory behavior based signal decomposition (IOBSD) | |
CN105760839A (en) | Bearing fault diagnosis method based on multi-feature manifold learning and support vector machine | |
CN113780055B (en) | MOMEDA and compression sensing rolling bearing fault diagnosis method | |
Loutas et al. | Utilising the wavelet transform in condition-based maintenance: A review with applications | |
He et al. | GMC sparse enhancement diagnostic method based on the tunable Q-factor wavelet transform for detecting faults in rotating machines | |
Dai et al. | Complex scale feature extraction for gearbox via adaptive multi-mode manifold learning | |
Feng et al. | A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network | |
CN112945546A (en) | Accurate diagnosis method for complex fault of gear box | |
CN107966287B (en) | Weak fault feature extraction method for self-adaptive electromechanical equipment | |
CN113639985B (en) | Mechanical fault diagnosis and state monitoring method based on optimized fault characteristic frequency spectrum | |
CN114707537A (en) | Rotary machine fault feature extraction method based on self-adaptive VMD and optimized CYCBD | |
CN103064821A (en) | Method and device for analyzing dynamic signals | |
Jena et al. | Multiple-teeth defect localization in geared systems using filtered acoustic spectrogram | |
CN110377927B (en) | Pump station unit rotor state monitoring method based on MATLAB simulation |