Abstract
This paper deals with vibration-fault diagnosis of spark plug of an internal combustion engine using wavelet analysis and support vector machine. In order to reduce the noises of the vibration signals, wavelet denoising technique was used. A performance comparison was made between different mother wavelets as well as different levels of decomposition in order to find the best cases for the system under study. The results showed that the maximum classification accuracies were obtained by 13 different wavelets, namely, db1-4, db1-5, db2-4, db3-4, coif1-4, coif1-5, coif2-4, coif3-3, coif3-4, coif3-5, dmey-2, dmey-4 and bior3.7-6. It was also demonstrated that db1, coif1, coif3 and dmey were valuable mother wavelets for this study. Moreover, the results indicated that the proposed approach can reliably be used for spark plug fault diagnosis.
| Original language | English |
|---|---|
| Journal | Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering |
| Volume | 231 |
| Issue number | 3 |
| Pages (from-to) | 359-370 |
| Number of pages | 12 |
| ISSN | 0954-4089 |
| DOIs | |
| Publication status | Published - 1 Jun 2017 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2017 Institution of Mechanical Engineers.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
Keywords
- feature extraction
- mother wavelets
- Spark-ignition engine
- support vector machine
- vibration analysis
- wavelet denoising