Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
National Engineering School of Tunis · Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies
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Abstract
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Citation impact
761
total citations
- FWCI
- 43.90
- Percentile
- 100%
- References
- 31
Citations per year
Authors
5- JBJaouher Ben AliCorresponding
National Engineering School of Tunis, Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies
- NFNader Fnaiech
National Engineering School of Tunis
- LSLotfi Saïdi
National Engineering School of Tunis
- BCBrigitte Chebel‐Morello
Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies
- FFFarhat Fnaiech
National Engineering School of Tunis
Topics & keywords
Topics
Keywords
- Hilbert–Huang transform
- Vibration
- Bearing (navigation)
- Artificial neural network
- Fault (geology)
- Condition monitoring
- Rolling-element bearing
- Pattern recognition (psychology)
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