Name
Title | ||
|---|---|---|
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks. |
Abstract | ||
|---|---|---|
Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering bot... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIE.2012.2219838 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Vectors,Biological neural networks,Vibrations,Support vector machine classification,Feature extraction,Shape | Pattern recognition,Visualization,Bearing (mechanical),Feature extraction,Control engineering,Artificial intelligence,Condition monitoring,Engineering,Vibration,Component analysis,Artificial neural network,Principal component analysis | Journal |
Volume | Issue | ISSN |
60 | 8 | 0278-0046 |
Citations | PageRank | References |
67 | 2.99 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel Delgado Prieto | 1 | 85 | 8.94 |
| Giansalvo Cirrincione | 2 | 121 | 13.13 |
| Antonio Garcia Espinosa | 3 | 110 | 9.18 |
| Juan Antonio Ortega Redondo | 4 | 208 | 29.56 |
| Humberto Henao | 5 | 169 | 13.02 |