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Motor's Early Fault Diagnosis Based on Support Vector Machine
| Content Provider | Scilit |
|---|---|
| Author | Li, Shu-Ying Xue, Lei |
| Copyright Year | 2018 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering An induction motors early fault diagnosis method was presented in this paper, based on Motor Current Spectrum Analysis (MCSA) and Support Vector Machine (SVM). After the stator current was sampled and transferred in FFT, the fault feature was extracted as the input of the SVM. The multi-fault SVM classifier was constructed based one-against-one strategy and mixed matrix combination, to perform the fault diagnosis and classification of different types of faults. Experiment results show that the method in this essay achieved good performance of classification under nonlinear, high dimension and small sample sets, which improved the accuracy in motor fault diagnosis. |
| Related Links | http://iopscience.iop.org/article/10.1088/1757-899X/382/3/032047/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/382/3/032047 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 3 |
| Volume Number | 382 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-07-12 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Industrial Engineering |
| Content Type | Text |
| Resource Type | Article |
