Gao et al., 2022 - Google Patents
Rolling bearing compound fault diagnosis based on parameter optimization MCKD and convolutional neural networkGao et al., 2022
- Document ID
- 10533081445719584265
- Author
- Gao S
- Shi S
- Zhang Y
- Publication year
- Publication venue
- IEEE Transactions on Instrumentation and Measurement
External Links
Snippet
For the sake of solving the problem of the difficulty of extracting fault features under the background of noise and accurately identify the state of the bearing, a compound fault diagnosis method of rolling bearing based on parameter optimization maximum correlated …
- 238000003745 diagnosis 0 title abstract description 41
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Rolling bearing compound fault diagnosis based on parameter optimization MCKD and convolutional neural network | |
| Wang et al. | A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN | |
| Li et al. | Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery | |
| Qiao et al. | Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads | |
| Gao et al. | Rolling bearing fault diagnosis based on CEEMDAN and refined composite multiscale fuzzy entropy | |
| CN112257530B (en) | Rolling bearing fault diagnosis method based on blind signal separation and support vector machine | |
| Du et al. | Fault diagnosis of rotating machinery based on 1D–2D joint convolution neural network | |
| Xue et al. | Feature extraction using hierarchical dispersion entropy for rolling bearing fault diagnosis | |
| Liu et al. | TScatNet: An interpretable cross-domain intelligent diagnosis model with antinoise and few-shot learning capability | |
| CN111089726B (en) | Rolling bearing fault diagnosis method based on optimal dimension singular spectrum decomposition | |
| Zhang et al. | A multi-module generative adversarial network augmented with adaptive decoupling strategy for intelligent fault diagnosis of machines with small sample | |
| CN106092574A (en) | The Method for Bearing Fault Diagnosis selected with sensitive features is decomposed based on improving EMD | |
| CN112461547B (en) | Bearing fault diagnosis method based on multi-island genetic and variation modal decomposition | |
| CN111665050B (en) | A Fault Diagnosis Method of Rolling Bearing Based on Clustering K-SVD Algorithm | |
| Zhang et al. | Toward small sample challenge in intelligent fault diagnosis: Attention-weighted multidepth feature fusion net with signals augmentation | |
| Liang et al. | Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery | |
| Feng et al. | A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network | |
| CN108896306A (en) | Method for Bearing Fault Diagnosis based on adaptive atom dictionary OMP | |
| Liu et al. | Composite multi-scale basic scale entropy based on CEEMDAN and its application in hydraulic pump fault diagnosis | |
| Li et al. | Research on fault diagnosis of highway Bi-LSTM based on attention mechanism | |
| Liu et al. | An Integrated Fault Identification Approach for Rolling Bearings Based on Dual‐Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude‐Aware Permutation Entropy | |
| Pang et al. | Clustering group-sparse mode decomposition and its application in rolling bearing fault diagnosis | |
| Zhou et al. | A novel rolling bearing fault diagnosis method based on continuous hierarchical fractional range entropy | |
| CN112485001A (en) | Fault diagnosis method for intelligent elevator | |
| Fan et al. | A GTI&Ada-act LMCNN method for intelligent fault diagnosis of motor rotor-bearing unit under variable conditions |