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EnglishRecurrence quantification analysis (RQA) allows to quantify the periodic behavior using recurrence plots instead of deriving information purely from visual analysis. The current study presents a preliminary analysis of stator-current measurements for electric motor fault detection and classification by means of the recurrence quantification theory. Firstly, a preliminary visual inspection of the recurrence plots of stator-current measurements for healthy and faulty electric motors is presented. Thereafter, the following RQ metrics are analyzed: the recurrence rate, the determinism, the divergence, the Shannon entropy, the laminarity and the trapping time. Then, the RQ metrics are used as predictors for fault detection and classification. The classification results (100% fault classification accuracy), which are presented using the linear support vector machine classifier, show that the RQA can be considered as a tool for motor current signature analysis.
| Autori: | |
| Autori: | Ferracuti, F; Freddi, A; Longhi, S; Monteriu, A |
| Titolo: | Recurrence Quantification Analysis of Stator-Current Measurements for Electric Motor Fault Classification |
| Numero degli autori: | 4 |
| Data di pubblicazione: | 2019 |
| Codice identificativo ISI: | WOS:000522050603114 |
| Codice identificativo Scopus: | 2-s2.0-85084067547 |
| Revisione (peer review): | Esperti anonimi |
| Lingua: | Inglese |
| Pagina iniziale: | 3691 |
| Pagina finale: | 3696 |
| Numero di pagine: | 6 |
| Abstract: | Recurrence quantification analysis (RQA) allows to quantify the periodic behavior using recurrence plots instead of deriving information purely from visual analysis. The current study presents a preliminary analysis of stator-current measurements for electric motor fault detection and classification by means of the recurrence quantification theory. Firstly, a preliminary visual inspection of the recurrence plots of stator-current measurements for healthy and faulty electric motors is presented. Thereafter, the following RQ metrics are analyzed: the recurrence rate, the determinism, the divergence, the Shannon entropy, the laminarity and the trapping time. Then, the RQ metrics are used as predictors for fault detection and classification. The classification results (100% fault classification accuracy), which are presented using the linear support vector machine classifier, show that the RQA can be considered as a tool for motor current signature analysis. |
| Parole Chiave: | recurrence quantification analysis; fault detection; electric motor; motor current signature analysis |
| Data di presentazione: | 2020-05-15T15:53:04Z |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11566/278775
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