Research Article
Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition
Table 5
Classification accuracy of different methods.
| | Method | Classification accuracy | Number of categories |
| | 80 features—softmax | 98.90% | 52 | | 80 features—SVM | 99.05% | 52 | | 91 features—softmax | 99.48% | 52 | | 91 features—SVM | 99.75% | 52 | | EMD-ANN [13] | 96.24% | 3 | | Wavelet-ANN [13] | 88.54% | 3 | | CNN with 2 pipelines [14] | 93.61% | 8 | | CNN with statistical feature [15] | 98.02% | 12 | | CNN with statistical feature [15] | 98.35% | 8 | | Hierarchical ADCNN [16] | 98.13% | 3 | | SVRM [16] | 94.17% | 3 | | 1D-CNN [17] | 97.40% | 2 | | WP-SVM [17] | 99.20% | 2 | | FFT-SVM [17] | 84.20% | 2 |
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