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.
Highlights
With the wide application of electronic systems in aerospace, aircraft, robot, and other fields, improving the stability, security, and maintainability of electronic systems has become a fundamental issue in the circuit field [1]
This paper proposes to employ the backward difference as the preprocessor and a novel variant of CNN, i.e., convolutional neural network with global average pooling (CNN-GAP) is taken for feature extraction and fault classification
As the conventional deep learning (DL)-based methods directly use the raw time domain signals as inputs, their performance is still inadequate for incipient fault diagnosis or a complex circuit diagnosis
Summary
With the wide application of electronic systems in aerospace, aircraft, robot, and other fields, improving the stability, security, and maintainability of electronic systems has become a fundamental issue in the circuit field [1]. Developing an effective analog circuit fault diagnosis method is of great significance for maintaining electronic systems’ reliable operation. Hard faults are mainly manifested as the circuit topology changing or the component value extremely exceeding the nominal value, resulting in complete circuit failure, while soft faults are caused by the deviation of the component value from the nominal value [5]. These soft faults do not cause the circuit to fail completely, they will affect the circuit performance. This paper mainly focuses on the soft fault diagnosis of analog circuits
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