Research Article
Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine
Table 5
True positive and false positive of classification accuracy with noise (SNR = 15 dB).
| Algorithm | Average classification accuracy in terms of true positive and false positive | Average | AMF | BRBF | NO | RIF | BF | BSF | PMF | PIF |
| Algorithm [11] | | | | | | | | | | TP | 91.45 | 88.00 | 84.00 | 90.91 | 96.00 | 98.91 | 95.19 | 79.82 | 90.535 | FP | 1.95 | 0.49 | 2.57 | 0.99 | 0.16 | 0.57 | 0.25 | 3.40 | 1.2975 | Algorithm [10] | | | | | | | | | | TP | 96.15 | 85.57 | 48.26 | 78.46 | 100.00 | 100.00 | 96.92 | 100.0 | 88.17 | FP | 0.00 | 10.83 | 0.73 | 0.03 | 0.00 | 0.000 | 3.18 | 3.65 | 2.3025 | Algorithm [4] | | | | | | | | | | TP | 34.42 | 0.00 | 7.50 | 100.00 | 100.00 | 100.00 | 91.73 | 91.92 | 65.69625 | FP | 0.00 | 0.00 | 7.45 | 23.74 | 2.31 | 4.53 | 0.00 | 0.00 | 4.75375 | Proposed approach | | | | | | | | | | TP | 100.0 | 94.61 | 100.0 | 100.00 | 100.00 | 100.00 | 100.00 | 100.0 | 100.00 | FP | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
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