Research Article
A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy
Table 2
Classification results of different classifiers with feature vectors extracted by different methods.
| Classifier | Testing accuracies with feature vectors extracted by different methods | DTCWPT + IMPE + LLTSA | DTCWPT + MPE + LLTSA | DTCWPT + IMPE | IMPE |
| ELM | 100% | 98% | 99.25% | 93% | SVM | 99.75% | 97.75% | 99.25% | 91.5% | ANN | 99% | 96% | 98.25% | 90.5% | KNNC | 99.5% | 97.75% | 99% | 91.5% | Average of four classifiers | 99.56% | 97.37% | 98.94% | 91.63% |
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