Paper Title:
Study of the Fault Diagnosis Method Based on Wavelet Time and Frequency Analysis
| Periodical | Advanced Materials Research (Volumes 472 - 475) |
|---|---|
| Main Theme | Advanced Manufacturing Technology |
| Edited by | Wenzhe Chen, Xipeng Xu, Pinqiang Dai, Yonglu Chen and Zhengyi Jiang |
| Pages | 2166-2170 |
| DOI | 10.4028/www.scientific.net/AMR.472-475.2166 |
| Citation | Qun Qi et al., 2012, Advanced Materials Research, 472-475, 2166 |
| Online since | February, 2012 |
| Authors | Qun Qi, Xue Zhang Zhao |
| Keywords | Fault Diagnosis, Wavelet Neural Network (WNN), Wavelet Transform (WT) |
| Price | US$ 28,- |
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Abstract
In order to better solve asynchronous motor complex fault characteristics, improve the reliability of the diagnosis and accuracy, combined with wavelet transform technique, construct a wavelet neural network, wavelet transform technology feature extraction asynchronous motor as a signal wavelet neural network's input vector, and the wavelet neural network algorithm was used to optimize, realize the motor identify types of fault, through the simulation experiment data diagnosis results show that this method is effective and feasible. Based on the wavelet analysis and neural network fault diagnosis method of research.
