Analog Circuit Fault Diagnosis Method Based on Characteristic Layer Fusion and Improved Potential Energy Function Classification

2013 ◽  
Vol 765-767 ◽  
pp. 2314-2318
Author(s):  
Xin Ying Li ◽  
Li Jie Sun ◽  
Li Zhang ◽  
Da Bo Zhang
Keyword(s):  
Fault Diagnosis ◽  
Potential Energy ◽  
Analog Circuits ◽  
Energy Function ◽  
Analog Circuit ◽  
Potential Energy Function ◽  
Fusion Method ◽  
Classification Problems ◽  
Reduction Effect ◽  
Circuit Fault Diagnosis

To circuit system with a transition state from normal to fault, this paper presents "sub-health" concept to describe it, and the experiments add sub-health diagnosis type. To problem of diagnosis difficulty caused by data overlapping due to tolerance existing in analog circuits, characteristic layer fusion method is selected for feature extraction, and put forward the distance evaluation factor for feature selection. Then potential energy function classification is adopted to diagnose faults, and principle of binary tree is combined with potential energy function classification to solve multiple classification problems. The experiments adopt BP neural network to compare and verify that the method proposed is effective. The results fully illustrate that characteristic layer fusion method can extract fault features effectively, distance evaluation factor has achieved a good dimension reduction effect, and improved potential energy function classification realizes soft fault diagnosis accurately.

scholarly journals An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

2020 ◽  
Vol 10 (7) ◽  
pp. 2386
Author(s):  
Sumin Guo ◽  
Bo Wu ◽  
Jingyu Zhou ◽  
Hongyu Li ◽  
Chunjian Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Classification Performance ◽  
Fault Classification ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Learning Machine ◽  
Circle Model

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

scholarly journals Fault Diagnosis of Analog Circuits Based on CFNN

2012 ◽  
Vol 6-7 ◽  
pp. 1045-1050
Author(s):  
Mei Rong Liu ◽  
Yi Gang He ◽  
Xiang Xin Li
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Local Optimum ◽  
Chaotic Mapping ◽  
Movement Characteristic ◽  
Fuzzy Neural ◽  
Global Movement ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

An analog circuits fault diagnosis method based on chaotic fuzzy neural network (CFNN) is presented. The method uses the advantage of the global movement characteristic inherent in chaos to overcome the shortcomings that BPNN is usually trapped to a local optimum and it has a low speed of convergence weights. The chaotic mapping was added into BPNN algorithm, and the initial value of the network was selected. The algorithm can effectively and reliably be used in analog circuit fault diagnosis by comparing the two methods and analyzing the results of the example.

Analog Circuit Fault Diagnosis Based on Support Vector Machine

2014 ◽  
Vol 1055 ◽  
pp. 99-102
Author(s):  
Jun Shi ◽  
Tong Shu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Wavelet Packet ◽  
Parameter Determination ◽  
Support Vector ◽  
Svm Classifier ◽  
Wavelet Packet Decomposition ◽  
Classification Problems ◽  
Circuit Fault Diagnosis

Analog circuit fault diagnosis is essentially a multiple state pattern classification problems. The traditional support vector machine classifier is for binary classification problems. In more than three kinds of commonly used class promotion model. This paper adopted the decision directed acyclic graph of multi-value classification algorithm. Multi-fault SVM classifier model is established. And the kernel function selection and nuclear parameter determination method were studied. Based on this model, support vector machine is used for analog circuit fault diagnosis given the basic idea and implementation steps. In analog circuit fault feature extraction technology, the effective sample point voltage amplitude response signal as well as the fault characteristic samples are extracted by wavelet packet decomposition of the energy spectrum method to extract the signal fault characteristics as the fault samples, formed based on effective sampling points. The SVM classifier is based on wavelet packet decomposition and the SVM classifier two methods of analog circuit fault diagnosis.

Analog Circuit Intelligent Fault Diagnosis Based on PCA and OAOSVM

2012 ◽  
Vol 468-471 ◽  
pp. 802-806 ◽  
Author(s):  
Ke Guo ◽  
Yi Zhu ◽  
Ye San
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Principal Component ◽  
Electronic Systems ◽  
Analysis Technique ◽  
Fault Features ◽  
Major Fault ◽  
Circuit Fault Diagnosis

Fault diagnosis of analog circuits is essential for guaranteeing the reliability and maintainability of electronic systems. Analog circuit fault diagnosis can be regarded as a pattern recognition issue and addressed by one-against-one SVM. In order to obtain a good SVM-based fault classifier, the principal component analysis technique is adopted to capture the major fault features. The extracted fault features are then used as the inputs of SVM to solve fault diagnosis problem. The effectiveness of the proposed method is verified by the experimental results.

scholarly journals Analog Circuit Fault Diagnosis Using a Novel Variant of aConvolutional Neural Network

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 17
Author(s):  
Liang Han ◽  
Feng Liu ◽  
Kaifeng Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Electronic Systems ◽  
Average Difference ◽  
Multi Scale ◽  
Novel Variant ◽  
Circuit Fault Diagnosis

Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.

Methods of Handling the Aliasing and Tolerance Problem for a New Unified Fault Modeling Technique in Analog-Circuit Fault Diagnosis

2014 ◽  
Vol 981 ◽  
pp. 11-16 ◽  
Author(s):  
Yuan Gao ◽  
Cheng Lin Yang ◽  
Shu Lin Tian
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Fault Modeling ◽  
Modeling Method ◽  
Soft Fault ◽  
The Difference ◽  
Circuit Fault Diagnosis ◽  
Nonlinear Analog Circuits ◽  
Nonlinear Analog

Soft fault diagnosis and tolerance are two challenging problems in analog circuit fault diagnosis. This paper proposes approaches to solve these two problems. First, a complex field modeling method and its theoretical proof are presented. This fault modeling method is applicable to both hard (open or short) and soft (parametric) faults. It is also applicable to either linear or nonlinear analog circuits. Then, the parameter tolerance is taken into consideration. A frequency selection method is proposed to maximize the difference between the faults fault signature. Hence, the aliasing problem arise from tolerance can be mitigated. The effectiveness of the proposed approaches is verified by simulated results.

Analog Circuit Fault Diagnosis Using LDA and OAOSVM Approach

2012 ◽  
Vol 490-495 ◽  
pp. 1130-1134 ◽  
Author(s):  
Ke Guo ◽  
Yi Zhu ◽  
Ye San
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Linear Discriminant ◽  
Analysis Technique ◽  
Fault Features ◽  
Major Fault ◽  
Circuit Fault Diagnosis

Fault diagnosis of analog circuits is essential for guaranteeing the reliability and maintainability of electronic systems. Analog circuit fault diagnosis can be regarded as a pattern recognition issue and addressed by Multi-class SVM. A novel diagnosis technique based on linear discriminant analysis and one-against-one SVM is proposed in the paper. In order to obtain a good SVM-based fault classifier, the linear discriminant analysis technique is adopted to capture the major fault features. The extracted fault features are then used as the inputs of one-against-one SVMs to solve fault diagnosis issue. The effectiveness of the proposed approach is demonstrated by the experimental results.

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