Research Article
Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine
Table 4
True positive and false positive of classification accuracy without noise.
| | Algorithm | Average classification accuracy in terms of true positive and false positive | Average | | AMF | BRBF | NO | RIF | BF | BSF | PMF | PIF |
| | Algorithm [11] | | | | | | | | | | | TP | 91.35 | 91.54 | 84.23 | 90.38 | 96.73 | 98.46 | 97.11 | 80.19 | 91.24875 | | FP | 1.54 | 1.24 | 2.17 | 0.80 | 0.22 | 0.47 | 0.63 | 3.24 | 1.28875 | | Algorithm [10] | | | | | | | | | | | TP | 98.85 | 93.85 | 90.00 | 100.00 | 100.00 | 100.00 | 96.73 | 99.04 | 97.30875 | | FP | 0.00 | 1.59 | 1.02 | 0.00 | 0.00 | 0.00 | 0.76 | 0.00 | 0.42125 | | Algorithm
[4]
| | | | | | | | | | | TP | 100.0 | 100.0 | 100.0 | 100.00 | 100.00 | 100.00 | 100.00 | 100.0 | 100 | | FP | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | | Proposed approach | | | | | | | | | | | TP | 100.0 | 100.00 | 100.00 | 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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