Analog Circuit Fault Diagnosis Method Based on Preferred Wavelet Packet and ELM

Haitao Shi, Qide Tan, Chenggang Li, Xiangyu Lv
2017 Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)   unpublished
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
more » ... 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. Keywords-wavelet packet transform; extreme learning machine (ELM); analog circuit; fault diagnosis; feature departure degree (3)
doi:10.2991/eame-17.2017.1 fatcat:fdyvndd3ored3nfshpgqmt3hqa