Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method
| Autor: | Si Wen Tang, Hua Kui Yin, Ling Li Jiang |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Fusion
Level fusion Engineering Bearing (mechanical) business.industry Pattern recognition General Medicine Structural engineering Fault (power engineering) law.invention Vibration Support vector machine law Pattern recognition (psychology) Feature (machine learning) Artificial intelligence business human activities |
| Zdroj: | Applied Mechanics and Materials. 273:260-263 |
| ISSN: | 1662-7482 |
| Popis: | Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic. Fault diagnosis is critical to maintaining the normal operation of the bearings. This paper proposes feature-level fusion method for rolling bearing fault diagnosis. Features are extracted from eight vibration signals to constitute a fusion vector. SVM is used for pattern recognition. The case study results show that the proposed method is useful for rolling bearing fault diagnosis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|