Abstract
Studies related with the induction motor bearings fault detection have been used digital signal processing and pattern recognition techniques. However, performance of these approaches depends on the use of correct features. This paper deals an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Discrimination was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.
| Original language | English (US) |
|---|---|
| Title of host publication | Applications of Computational Intelligence - First IEEE Colombian Conference, ColCACI 2018, Medellín, Colombia, May 16–18, 2018, Revised Selected Papers |
| Editors | Alvaro David Orjuela-Cañón, Juan Carlos Figueroa-García, Julián David Arias-Londoño |
| Publisher | Springer |
| Pages | 50-62 |
| Number of pages | 13 |
| ISBN (Print) | 9783030030223 |
| DOIs | |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia Duration: May 16 2018 → May 18 2018 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 833 |
| ISSN (Print) | 1865-0929 |
Conference
| Conference | 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 |
|---|---|
| Country/Territory | Colombia |
| City | Medellin |
| Period | 5/16/18 → 5/18/18 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Mathematics
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