Abnormal Detection of Household Appliances Based on Wavelet Denoising and DAG-SVM
| Autor: | Xiao Wang, Geng-ren Zuo, Bo Yin, Zhao Li, Tao Wang, Ming-xing Gao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Noise (signal processing)
business.industry Computer science Noise reduction ComputerApplications_COMPUTERSINOTHERSYSTEMS Pattern recognition Fault (power engineering) Signal Support vector machine ComputingMethodologies_PATTERNRECOGNITION Wavelet Interference (communication) Wavelet denoising Artificial intelligence business |
| Zdroj: | DEStech Transactions on Engineering and Technology Research. |
| ISSN: | 2475-885X |
| Popis: | In order to solve the problem of abnormal detection of household appliances and realize the extraction of abnormal signal features of household appliances and the positioning of the time, this paper proposes an abnormal processing method based on wavelet denoising and DAG-SVM. The noise is eliminated by the wavelet threshold denoising method, which reduces the interference of the noise to the fault signal. Thus, the real signal can be reduced from the signal of strong noise. The abnormal signal of household electrical appliances was extracted after noise reduction. These characteristics are input into DAG-SVM for training and modeling. The validity of this method is verified by simulation and experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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