A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN

Abstract

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model

“…When performing feature extraction, the TQWT is used for the multi-band decomposition of the The approach suggested herein was compared with the classical fault diagnosis methods, as shown in Table 5. The TQWT-CNN [38] model uses an improved TQWT and CNN-LSTM network to conduct characteristic extraction and uses a CNN network to complete the fault diagnosis; FRFT-CNN [39] uses the fractional Fourier transform to transform the signal's statistical characteristic features as the fault features, and then uses KPCA to downscale the features to obtain the optimal features, and finally uses a Convolutional Neural Network for fault classification. GMKL-SVM [40] extracts the wavelet coefficient energy of the signal as the features and then uses the Particle Swarm Optimization (PSO) optimized Generalized Multi Kernel Learning-Support Vector Machine (GMKL-SVM) for fault classification.…”

“…This paper 100% 99.09% TQWT-CNN [38] 99.10% 98.96% FRFT-CNN [39] 98.46% 95.15% GMKL-SVM [40] 100% 97.58% IEEMD-SVM [14] 98.72% 96.36% Improved WT-MKELM [21] 100% 98.75%…”