Analog circuit soft-fault diagnosis based on sensitivity analysis with minimum fault number rule

Ji, Lipeng; Hu, Xiaohui

doi:10.1007/s10470-018-1111-y

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…With the increase in the training times of the samples, the average error becomes smaller and smaller, indicating that the weights of training samples tend towards the optimal weight vector, and the data fusion precision gradually improves. As shown in the figure, when the improved BP neural network data fusion algorithm based on weight adjustment is used, the sample converges faster in the training process and the training time of the sample can be shortened [12]. Although the average error of the samples of the BP neural network data fusion algorithm before and after the weight adjustment and improvement is the same at the beginning of the training, the average error value of the improved sample after 15 training sessions is close to 0.7, less than 0.8 before the algorithm improvement, indicating that the sample fusion accuracy under the improved algorithm is higher [13].…”

Section: Total Time 3hourmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

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Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

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Section: Total Time 3hourmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

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“…coefficients where N and K are the power of set (9) and the assumed order of the polynomials, respectively.…”

Section: Structure Of the Regression Modelsmentioning

confidence: 99%

“…A wide range of methods for testing the functional parameters of analog circuits have been proposed in the literature. An optimization formulation approach is a commonly used tool that is based directly on the circuit structure [9]. It is intuitive and proves to be effective on many occasions.…”

Section: Introductionmentioning

confidence: 99%

Design an Identification Function to Reduce the Computational Resources on the Testing Process of an Analog Electronic Circuit

Temich¹,

Golonek²,

Grzechca³

2019

ElAEE

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In modern electronic circuits, imperfectness in the technological process can cause errors in reaching the correct values of the functional parameters. In order to solve this problem, a novel approach of analog and mixed-signal circuit testing methodology is used. The presented approach allows the testing complexity to be reduced and the testing time to be decreased. For this paper, selected signal features were designated from the transient output signal response. Using regression models with the extracted signal features, the functional parameters of a circuit were determined. An evolutionary determination of the regression models enabled the efficiency of the identification process to be maximized. The proposed methodology is presented for an exemplary CMOS Dickson charge pump circuit.

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“…Thus, the problem of fault diagnosis is of great significance to improving reliability and safety of analog electronic circuits. Although numerous publications including books, (Gizopoulos, 2006;Kabisatpathy, et al, 2005;Sun, 2008), and journal publications, (Binu and Kariyappa, 2017;Deng and Liu, 2017;Ji and Hu, 2018;Papakostas and Hatzopoulos, 2010;Sindia et al, 2012;Tadeusiewicz et al, 2015aTadeusiewicz et al, , 2015bXie et al, 2015;Yuan et al, 2010) refer to this problem the diagnosis still heavily relies on the engineer's experience and is a difficult task, especially for the soft fault diagnosis of several parameters. The complete fault diagnosis includes three aspects as follows: detection of any fault in the circuit under test (CUT), identification of faulty elements, and determination of the parameter values relating to the faulty elements.…”

Section: Introductionmentioning

confidence: 99%

A method for fault diagnosis of nonlinear circuits

Tadeusiewicz

Hałgas

2019

COMPEL

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Purpose The purpose of this paper is to develop a method for multiple soft fault diagnosis of nonlinear circuits including fault detection, identification of faulty elements and estimation of their values in real circumstances. Design/methodology/approach The method for fault diagnosis proposed here uses a measurement test leading to a system of nonlinear equations expressing the measured quantities in terms of the circuit parameters. Nonlinear functions, which appear in these equations are not given in explicit analytical form. The equations are solved using a homotopy concept. A key problem of the solvability of the equations is considered locally while tracing the solution path. Actual faults are selected on the basis of the observation that the probability of faults in fewer number of elements is greater than in a larger number of elements. Findings The results indicate that the method is an effective tool for testing nonlinear circuits including bipolar junction transistors and junction field effect transistors. Originality/value The homotopy method is generalized and associated with a restart procedure and a numerical algorithm for solving differential equations. Testable sets of elements are found using the singular value decomposition. The procedure for selecting faulty elements, based on the minimal fault number rule, is developed. The method comprises both theoretical and practical aspects of fault diagnosis.

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Analog circuit soft-fault diagnosis based on sensitivity analysis with minimum fault number rule

Cited by 10 publications

References 20 publications

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Design an Identification Function to Reduce the Computational Resources on the Testing Process of an Analog Electronic Circuit

A method for fault diagnosis of nonlinear circuits

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