Incipient fault diagnosis of analog circuits based on wavelet transform and improved deep convolutional neural network

To enhance the reliability of analog circuits in electrical systems, this letter proposes a novel incipient fault diagnosis method by integrating wavelet transform(WT) and improved convolutional neural network. Different from traditional methods, where feature extraction and classification are separately designed and performed, this letter aims to automatically learn fault features and classify the type of faults simultaneously. An improved convolutional neural network named multi-channel compactness convolutional neural network (MC-CNN) is proposed, which can obtain complementary and rich diagnosis information from multi-scale components extracted by wavelet transform. Moreover, we adopt center loss as an auxiliary loss function to maximize the interclass separability and intraclass compactness of samples. The proposed method is fully evaluated with the Sallen-Key bandpass filter circuit and the four-opamp biquad high-pass filter circuit. The experimental results demonstrate that the proposed method is very effective in feature extraction for fault diagnosis, and has higher diagnosis accuracy than other typical fault diagnosis methods.