Research Article
Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
Table 1
A list of acronyms used in this paper.
| Acronym | Full form |
| DTCWPT | Dual-tree complex wavelet packet transform | TSFSR | Transferable sensitive feature selection by the ReliefF and sum of mean deviation | LMMC | Local maximum margin criterion | REBs | Rolling element bearings | EMD | Empirical mode decomposition | STFT | Short-time Fourier transform | WVD | Wigner–Ville distribution | WT | Wavelet transform | PTFT | Parameterized time-frequency transform | IRF | Instantaneous rotation frequency | SCT | Spline-kernelled chirplet transform | PCT | Polynomial chirplet transform | CWT | Continuous wavelet transform | DWT | Discrete wavelet transform | DTCWT | Dual-tree complex wavelet transform | PV | Peak value | RMS | Root mean square | V | Variance | Sw | Skewness | K | Kurtosis | HES | HHT envelope spectrum | RNN | Recurrent neural network | FEM | Finite element method | TL | Transfer learning | SMD | Sum of within-class mean deviations | TCA | Transfer component analysis | PCA | Principal component analysis | LDA | Linear discriminant analysis | KPCA | Kernel principal component analysis | LE | Laplacian eigenmaps | LLE | Local linear embedding | LPP | Locality-preserving projections | NPE | Neighborhood-preserving embedding | SSS | Small size sample | LFDA | Local Fisher discriminant analysis | MMC | Maximum margin criterion | MMD | Maximum mean discrepancy | MD | Mean deviations | WV | Weight value | SFS | Sensitive feature set | CWRU | Case Western Reserve University | OFS | Original feature set |
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