Fault detection for linear analog IC-the method of short-circuit admittance parameters

In nowadays, fault diagnosis method for analog circuit based on support vector machines, has become a hot topic in research field of fault diagnosis. However, in practical application of this method, the imbalanced problem occurred in fault sample dataset has greatly influenced its effectiveness. To remedy this problem, this paper proposed an improved Support Vector Machines method based on biased empirical feature mapping. In the new method, biased discriminant analysis was applied in empirical feature space, to make all normal samples far away from center of fault samples, so that the overall fault diagnosis ability can be improved. Through theoretical analysis and empirical study on actual electronic circuit fault diagnosis problem, we show that our method augments the diagnosis accuracy rate effectively.

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“…The rank of K is r, it is easy to know r≤m. Because K is a semi-definite matrix, it can be written as (1).…”

Section: Feature Mappingmentioning

confidence: 99%

“…Compared with successful digital circuit fault diagnosis, there are still no effective fault diagnosis methods for analog circuit until present [1] . This is because analog circuit lacks good fault model and there are complex nonlinear relationships between circuit response and component parameter, and etc.…”

Section: Introductionmentioning

confidence: 99%

Electronic circuit fault diagnosis methods based on improved Support Vector Machines

Yang

Wang

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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“…In essence, the diagnosis in analog circuit correspond to pattern recognition and classification problem [3] , the operating states are separated into normal and abnormal, whereas, which fault pattern the abnormal states belong to at all is a pattern recognition problem. Therefore, neural network 1-4244-0332-4/06/$20.00 ©2006 IEEE provides a completely novel problem-solving scheme for fault diagnosis in analog circuit.…”

Section: The Diagnosis In Analog Circuit Based On Wavelet-neural Nementioning

confidence: 99%

Research on Fault Diagnosis in Analog Circuit Based on Wavelet-Neural Network

Yuan

Chen

San-bao

et al. 2006

2006 6th World Congress on Intelligent Control and Automation

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The fault diagnosis method based on waveletneural network in analog circuit was presented. The problems on feature extraction in analog circuit, data pre-processing, training and testing of network, fault pattern classification were all discussed here. Fault features were extracted by circuit simulation, after data preprocessed by wavelet-neural network, which can be constructed into samples aggregation, the samples for training and testing were used to train and test neural network respectively, the uniform pattern samples were used to diagnose faults, the fault diagnosis in analogy circuit was realized effectively. An illustration validated this method.

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“…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]. Analog circuits are an essential part of electronic systems, although only 20% of electronic systems are analog circuits, 80% of electronic system failures are caused by them [2,3]. Therefore, developing an effective analog circuit fault diagnosis method is of great significance for maintaining electronic systems' reliable operation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Fault detection for linear analog IC-the method of short-circuit admittance parameters

Cited by 52 publications

References 6 publications

Electronic circuit fault diagnosis methods based on improved Support Vector Machines

Electronic circuit fault diagnosis methods based on improved Support Vector Machines

Research on Fault Diagnosis in Analog Circuit Based on Wavelet-Neural Network

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

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