Information Fusion Feature Preprocessor based on FRFT for Analog Circuits Fault Diagnosis
This paper presents a new fault feature preprocessor method for analog circuit fault diagnosis. An information fusion method based on fra ctional Fourier transform (FRFT) is introduced to extract features from voltages of the circuit under test (CUT). Firstly, the voltage signals gathered from test nodes of the CUT are preprocessed by FRFT, the fractional order p of the FRFT changes from 0 to 1 with a given step. Then, we gain the amplitudes of the transformed signals in fractional space and extract the mutual information entropies as features by a defined division scale. After normalization, the extracted features are used to train a neural network to diagnose faulty components in the CUT. The proposed feature preprocessor method is applied to two CUTs and is compared with three ordinary preprocessing methods in analog circuit fault diagnosis. The experiment results reveal that the proposed method can simplify the structure of the network and improve the diagnosis performance.
analog circuits fault diagnosis feature extraction information fusion fractional Fourier transform
Luo Hui Wang Youren Lin Hua Jiang Yuanyuan
College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nangjin 210016, College of Electric and Information Engineering,Anhui University of Science and Technology,Huainan 2
国际会议
2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)
成都
英文
1232-1237
2011-08-16（万方平台首次上网日期，不代表论文的发表时间）