The Study of Test Stimulus Optimization of Analog Circuit Based on AS-PSO Hybrid Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 582-587
Author(s):  
Hai Jun Lin ◽  
Qi Gao Wang ◽  
Ting Yu Sun ◽  
Ming Chao Dai ◽  
Xu Hui Zhang
Keyword(s):  
Analog Circuits ◽  
Hybrid Algorithm ◽  
Euclidean Distance ◽  
Analog Circuit ◽  
Fitness Function ◽  
Volterra Kernel ◽  
Novel Approach ◽  
Swarm Algorithms ◽  
Faults Diagnosis ◽  
Circuit Fault Diagnosis

This paper proposes a novel approach to diagnosis the faults in analog circuits based on Volterra kernel and ant colony-particle swarm algorithms. In the analog circuit fault diagnosis, we use the Volterra kernel as the feature vector which makes the characteristic vector lumped Euclidean distance in serials of fault states under the same excitation signals as the fitness function. And the optimized the parameters are used to stimulate the multi- frequency sinusoidal signal. The AS-PSO hybrid algorithm is performed to find the best excitation signal parameters. Experimental results show that the proposed approach can achieve good faults diagnosis results.

scholarly journals A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 349
Author(s):  
Igor Aizenberg ◽  
Riccardo Belardi ◽  
Marco Bindi ◽  
Francesco Grasso ◽  
Stefano Manetti ◽  
...  
Keyword(s):  
Analog Circuits ◽  
Analog Circuit ◽  
Machine Learning Techniques ◽  
Rigorous Approach ◽  
Testability Analysis ◽  
Learning Techniques ◽  
Fabrication Tolerances ◽  
Circuit Fault Diagnosis ◽  
Faulty Component ◽  
Ambiguity Groups

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

A novel approach for analog circuit fault diagnosis based on Deep Belief Network

Measurement ◽  
2018 ◽  
Vol 121 ◽  
pp. 170-178 ◽  
Author(s):  
Guangquan Zhao ◽  
Xiaoyong Liu ◽  
Bin Zhang ◽  
Yuefeng Liu ◽  
Guangxing Niu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Belief Network ◽  
Novel Approach ◽  
Circuit Fault Diagnosis

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1049-1052
Author(s):  
Bao Ru Han ◽  
Jing Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Algorithm ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Inertia Weight ◽  
High Convergence Rate ◽  
Swarm Algorithm ◽  
Circuit Fault Diagnosis

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

Fault Diagnosis of Analog Circuits with Tolerance Based on Simulation-before-Test and Distance Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 156-160
Author(s):  
Yong Jie Zhang ◽  
Jian Jun Hou ◽  
Liang Huang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Euclidean Distance ◽  
Analog Circuit ◽  
Practical Method ◽  
Fault Dictionary ◽  
Advantages And Disadvantages ◽  
Distance Methods

Fault dictionary is the most practical method of fault diagnosis in analog circuit. Before analog circuit with tolerance is diagnosed, circuit is simulated by computer. Typical parameters of every state are used to build fault dictionary. Distance algorithm is used to calculate the similarity between current circuit and every state of fault dictionary. Analog circuit with tolerance can be diagnosed by the distance. Firstly, the method of simulation-before-test is introduced to build fault dictionary. Secondly, familiar distance algorithm is resumed, such as Euclidean distance. Finally, an example of fault diagnosis of analog circuit with tolerance is provided. In the example, simulation-before-test and distance algorithm are combined to diagnose analog circuit with tolerance. Two distance methods are compared to explain the advantages and disadvantages of the Euclidean distance algorithm.

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.

scholarly journals Multiobjective Optimization Tool for a Free Structure Analog Circuits Design Using Genetic Algorithms and Incorporating Parasitics

2008 ◽  
Vol 2008 ◽  
pp. 1-9
Author(s):  
Yaser M. A. Khalifa ◽  
Badar Khan ◽  
Faisal Taha
Keyword(s):  
Genetic Algorithms ◽  
Circuit Design ◽  
Analog Circuits ◽  
Degrees Of Freedom ◽  
Analog Circuit ◽  
Search Space ◽  
Design Stage ◽  
Fitness Evaluation ◽  
Novel Approach ◽  
Specific Frequency

This paper presents a novel approach for a free structure analog circuit design using genetic algorithms (GAs). A major problem in a free structure circuit is its sensitivity calculations as a polynomial approximation for the design is not available. A further problem is the effect of parasitic elements on the resulting circuit's performance. In a single design stage, circuits that are produced satisfy a specific frequency response specifications using circuit structures that are unrestricted and with component values that are chosen from a set of preferred values including their parasitic effects. The sensitivity to component variations for the resulting designs is performed using a novel technique and is incorporated in the fitness evaluation function. The extra degrees of freedom resulting form unbounded circuit structures create a huge search space. The application chosen is an RLC ladder filters circuit design.

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.

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