A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

Abstract

This paper develops a novel soft fault diagnosis approach for analog circuits. The proposed method employs the backward difference strategy to process the data, and a novel variant of convolutional neural network, i.e., convolutional neural network with global average pooling (CNN-GAP) is taken for feature extraction and fault classification. Specifically, the measured raw domain response signals are firstly processed by the backward difference strategy and the first-order and the second-order backward difference sequences are generated, which contain the signal variation and the rate of variation characteristics. Then, based on the one-dimensional convolutional neural network, the CNN-GAP is developed by introducing the global average pooling technical. Since global average pooling calculates each input vector’s mean value, the designed CNN-GAP could deal with different lengths of input signals and be applied to diagnose different circuits. Additionally, the first-order and the second-order backward difference sequences along with the raw domain response signals are directly fed into the CNN-GAP, in which the convolutional layers automatically extract and fuse multi-scale features. Finally, fault classification is performed by the fully connected layer of the CNN-GAP. The effectiveness of our proposal is verified by two benchmark circuits under symmetric and asymmetric fault conditions. Experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

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

“…Electromagnetic circuits can be separated into analog circuits and digital circuits. Digital circuits have a lower frequency of failures, and fault diagnosis techniques are well established, so the reliability of the circuit system primarily focuses on analog circuits [2,3].…”

Analog Circuit Incipient Fault Detection Based on Attention Mechanism and Fully Convolutional Network

“…Zhang et al [23] applied a backward difference technique for data preprocessing and employed convolutional neural network global average pooling (CNNGAP) for feature extraction and fault classification. Yang et al [24] proposed a dual-input model based on a multiscale self-normalizing compressed excitation module convolutional neural network.…”

Analog circuit fault diagnosis based on feature attention

“…Yang et al applied a one-dimensional CNN to propose an intelligent feature acquisition approach for analog circuit diagnosis, which is reasonably applicable in complex analog circuits and has good anti-interference performance due to its compact structure and configuration [23]. Zhang et al used a soft fault diagnosis method for analog circuits that processes data using a backward differencing strategy and uses a new variant of convolutional neural networks, i.e., convolutional neural networks with a global average pooling layer, for feature extraction and fault classification [24]. Shokrolahi et al proposed a deep CNN method for fault detection using the real component of the power spectral intensity of the fault signal provided as the input image to the CNN to realize fault diagnosis [18].…”