A novel fault diagnosis method for analog circuits with noise immunity and generalization ability

Gao, Tianyu; Yang, Jingli; Jiang, Shouda

doi:10.1007/s00521-021-05810-4

Cited by 13 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the standard analog filter circuits in ISCAS'97 are selected for validation which are widely used as verification circuits in the field of analog circuit fault diagnosis. Among them, the Sallen-Key band-pass filter circuit, the CSTV filter circuit, and the leap-frog low-pass filter circuit are the most typical ones, which are often selected as verification circuits in most literature, such as [8,10,13,[25][26][27][28], etc. Therefore, these three representative circuits are also chosen for experimental verification in this section.…”

Section: Experimental Validationmentioning

confidence: 99%

“…Differences in manufacturing processes introduce tolerances to components in analog circuits; therefore, the tolerance of resistance and capacitance is set at 5% in this paper. When the sensitive components in the analog circuit operate within the tolerance range, the circuit is in a normal state; when the sensitive components in the circuit deviate from the nominal value by 5-10% the components start to fail, but the circuit can still operate normally; and when the sensitive components in the circuit deviate from the nominal value by 30-50%, the circuit malfunctions [19,[25][26][27][28]. To diagnose the faults in this circuit, the 30% deviation of the sensitive component and 35% deviation of the less sensitive component or confusion-prone component from the nominal value are taken as the standard of components' fault setting in this article.…”

Section: Circuit Under Testmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

Liu

Xie

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Analog circuits play an essential role in electronic systems. To strengthen the reliability of sophisticated electronic circuits, this paper proposes a novel analog circuit fault diagnosis method. Compared with traditional fault diagnosis, the fault diagnosis process in this paper uses a square wave as the stimulus of the circuit under test (CUT), which is beneficial for obtaining the response of the CUT with rich time and frequency domain information. The improved empirical wavelet transform (EWT), which can more accurately extract the amplitude modulated–frequency modulated (AM-FM) components, is used to preprocess the original response. Finally, based on the preprocessed data, a multi-input deep residual network (ResNet) is constructed for fault feature extraction and fault classification. The multi-input ResNet is a powerful approach for learning the fault characteristics of the CUT under different faults by learning the characteristics of the AM-FM components. The effectiveness of the method proposed in this paper is verified by comparing different fault diagnosis methods.

show abstract

Section: Experimental Validationmentioning

confidence: 99%

Section: Circuit Under Testmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

Liu

Xie

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, this paper only considers a single fault. In analog circuits, the circuit is in a normal state when components are operating within tolerance; when component parameters deviate from the nominal value by 10-30%, the circuit experiences an early soft fault; when the component parameters deviate from the nominal value by 30%-50%, the circuit experiences a soft fault [5,10,[31][32][33]. In this paper, we take the 30% deviation of the parameters of components from the nominal value as the standard of component fault setting.…”

Section: Circuit Under Testmentioning

confidence: 99%

A Novel Analog Circuit Fault Diagnosis Method Based on Multi-channel 1D-Resnet and Wavelet Packet Transform

Zhou

Tang

Liang

2022

Preprint

View full text Add to dashboard Cite

Fault diagnosis techniques for analog circuits are widely applied in the diagnosis of electronic devices. It is of great significance to accurately and quickly find the fault location and the deviation of fault parameters in analog circuits. In this paper, we propose an innovative fault diagnosis approach for analog circuits. In this method, the wavelet packet transform (WPT) is employed to preprocess the time-domain response signals of analog circuit, and the proposed multi-channel one-dimensional residual networks (MC-1D-ResNet) is utilized for feature mining and fault diagnosis. Specifically, the two-level WPT is first carried out on the measured time-domain response signal to generate one approximate signal and three detailed signals. Additionally, the MC-1D-ResNet is developed by introducing a multi-channel structure based on 1D-ResNet, which can excavate and fuse various features from response signals and realize fault diagnosis. Through the simulation of the Sallen-key bandpass filter circuit and Four-opamp biquad high-pass filter circuit, the diagnostic reliability of this method has been fully verified. The results show that this method is very valuable for diagnosing faults of analog circuits and performs well on both test circuits. Even in the complex Four-opamp biquad high-pass filter circuit, the diagnostic accuracy can be up to 99.74%.

show abstract

“…Then, based on the Parzen window density estimation method, an abnormal detection threshold was obtained by probabilistic density estimation of the healthy MD dataset. Gao et al [22] combined denoising autoencoders with sparse autoencoders, performed linear dimensionality reduction through linear discriminant analysis to obtain the most significant clustering features of signals, and finally classified faults using SVM.…”

Section: Introductionmentioning

confidence: 99%

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

Miao,

Zhang,

Chen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Owing to the tolerance of circuit components in analogue circuits, feature overlap occurs between different component faults, especially in the case of early-stage faults. To address this issue, we present an improved model that utilizes the channel attention mechanism in conjunction with a fully convolutional network. The proposed model conducts end-to-end diagnosis by utilizing the response signals generated from the tested circuit. To diagnose different fault categories, we add a global average pooling layer to the last layer of the fully convolutional network to obtain the probabilities of various fault categories. Furthermore, we incorporating a channel attention, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones, within the network, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones. The proposed method is validated with four-opamp biquad high-pass filter circuit and leapfrog filter circuit. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 95.24% in Case 1,87.09% in Case 2. This method exhibits higher diagnostic accuracy and reliability than existing methods.

show abstract

A novel fault diagnosis method for analog circuits with noise immunity and generalization ability

Cited by 13 publications

References 31 publications

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

A Novel Analog Circuit Fault Diagnosis Method Based on Multi-channel 1D-Resnet and Wavelet Packet Transform

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

Contact Info

Product

Resources

About