Invention Grant
- Patent Title: Analog-circuit fault diagnosis method based on continuous wavelet analysis and ELM neural network
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Application No.: US16088079Application Date: 2017-01-06
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Publication No.: US11280826B2Publication Date: 2022-03-22
- Inventor: Yigang He , Wei He , Qiwu Luo , Zhigang Li , Tiancheng Shi , Tao Wang , Zhijie Yuan , Deqin Zhao , Luqiang Shi , Liulu He
- Applicant: HEFEI UNIVERSITY OF TECHNOLOGY
- Applicant Address: CN Anhui
- Assignee: HEFEI UNIVERSITY OF TECHNOLOGY
- Current Assignee: HEFEI UNIVERSITY OF TECHNOLOGY
- Current Assignee Address: CN Anhui
- Agency: JCIPRNET
- Priority: CN201611243708.5 20161229
- International Application: PCT/CN2017/070351 WO 20170106
- International Announcement: WO2018/120283 WO 20180705
- Main IPC: G01R31/28
- IPC: G01R31/28 ; G01R31/316

Abstract:
An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification. The method of the invention has a better effect on extracting the circuit fault features and can be used to implement circuit fault classification accurately and efficiently.
Public/Granted literature
- US20200300907A1 ANALOG-CIRCUIT FAULT DIAGNOSIS METHOD BASED ON CONTINUOUS WAVELET ANALYSIS AND ELM NETWORK Public/Granted day:2020-09-24
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