Analog Circuit Fault Diagnosis Method Based on Preferred Wavelet Packet and ELM
- 10.2991/eame-17.2017.1How to use a DOI?
- wavelet packet transform; extreme learning machine (ELM); analog circuit; fault diagnosis; feature departure degree
In order to improve the effectiveness of fault feature extraction and achieve the accurate classification of fault patterns in analog circuit, the paper proposed a new analog circuit fault diagnosis method based on preferred wavelet packet and extreme learning machine (ELM). The concept of feature departure degree is defined, which can be used as a measure of wavelet packet transform to obtain the fault features using different wavelet basis function, and the wavelet basis function with maximum feature departure degree is selected and used to extract the fault feature. Further, the ELM is introduced for fault classification and identification, and the diagnosis result is compared with those using three popular neural networks. The simulation results show that the better diagnosis precision can be achieved using the preferred wavelet packet, and the test time and the classification precision of the ELM are all better than those using other methods.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Haitao Shi AU - Qide Tan AU - Chenggang Li AU - Xiangyu Lv PY - 2017/04 DA - 2017/04 TI - Analog Circuit Fault Diagnosis Method Based on Preferred Wavelet Packet and ELM BT - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) PB - Atlantis Press SP - 1 EP - 4 SN - 2352-5401 UR - https://doi.org/10.2991/eame-17.2017.1 DO - 10.2991/eame-17.2017.1 ID - Shi2017/04 ER -