Application of SVM based on FOA optimization in fault diagnosis of rotating machinery
| Autor: | Siteng Wang, Huawei Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Feature vector Feature extraction 020207 software engineering Pattern recognition 02 engineering and technology Wavelet packet decomposition Vibration Support vector machine Axle ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Artificial intelligence Complex wavelet transform business Classifier (UML) |
| Zdroj: | 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). |
| DOI: | 10.1109/iaeac.2017.8054467 |
| Popis: | The data shows that about 20% of car failures come from the rear axle of the car. Accordingly we use a support vector machine based on optimization algorithm of Drosophila melanogaster as the fault diagnosis method. The vibration signal is denoised by double-tree complex wavelet transform. The feature extraction is performed by wavelet packet decomposition, and the extracted feature vector is taken as the input data. The support vector machine (SVM) optimized by FOA is used as the classifier to obtain the feature vector of the collected vibration signal to get fault recognition rate. Experimental results show that this method has higher diagnostic accuracy than some other SVMs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|