Abstract
The engine vibration signals characters are extracted using wavelet packet technology. A model of wavelet neural networks is constructed based on wavelet frame theory and neural networks technology. Then multiresolution analysis is used to choose and optimize the wavelet neuron. The model is validated through the testing that simulates the faults of engine valve clearance. The experimental results show that the proposed automobile engine fault diagnostic model based on wavelet neural networks can diagnose the engine fault effectively.
| Original language | English |
|---|---|
| Pages | 1766-1770 |
| Number of pages | 5 |
| State | Published - 2004 |
| Event | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004 |
Conference
| Conference | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings |
|---|---|
| Country/Territory | China |
| City | Hangzhou |
| Period | 15/06/04 → 19/06/04 |
Keywords
- Fault diagnosis
- Feature extraction
- Multiresolution analysis
- Vibration signal
- Wavelet neural networks
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