Fault Diagnosis Of Electronic Circuits Research Articles

Fault Diagnosis of Power Electronic Circuits Based on Improved Particle Swarm Optimization Algorithm Neural Network

Fault Diagnosis of Power Electronic Circuits Based on Improved Particle Swarm Optimization Algorithm Neural Network

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

Research on Electronic Circuit Fault Diagnosis Method Based on SWT and DCNN-ELM

Research on Electronic Circuit Fault Diagnosis Method Based on SWT and DCNN-ELM

Intelligent Detection and Diagnosis of Power Failure Relying on BP Neural Network Algorithm.

The development of economy and the needs of urban planning have led to the rapid growth of power applications and the corresponding frequent occurrence of power failures, which many times lead to a series of economic losses due to failure to repair in time. To address these needs and shortcomings, this paper introduces a BP neural network algorithm to determine the neural network structure and parameters for fault diagnosis of power electronic inverter circuits with improved hazard. By optimizing the weights and thresholds of neural networks, the learning and generalization ability of neural network fault diagnosis systems can be improved. It can effectively extract fault features for training, sort out the business logic of power supply intelligent detection, analyze the potential hazards of power supply, and effectively perform circuit intelligent control to achieve effective fault detection of power supply circuits. It can provide timely feedback and hints to improve the fault identification ability and the corresponding diagnosis accuracy. Simulation results show that the method can eventually determine the threshold value for intelligent power fault detection and diagnosis by analyzing the convergence of long-term relevant indicators, avoiding the blindness of subjective experience and providing a theoretical basis for intelligent detection and diagnosis.

Design of electronic circuit fault diagnosis based on artificial intelligence

Design of electronic circuit fault diagnosis based on artificial intelligence

Design of electronic circuit fault diagnosis based on artificial intelligence

Design of electronic circuit fault diagnosis based on artificial intelligence

Electronic Circuit Fault Diagnosis and Prediction Technology Analysis

Electronic Circuit Fault Diagnosis and Prediction Technology Analysis

Fault Diagnosis of Linear Analog Electronic Circuit Based on Natural Response Specification using K-NN Algorithm

This paper reports a novel method for parametric fault diagnosis in linear analog electronic circuits using distance weighted cosine K-Nearest Neighbours (K-NN) algorithm that performs data classification on the basis of cosine similarity between data features or attributes. In this approach the analog electronic Circuit Under Test (CUT) is represented in the form of a transfer function model and natural response specifications of the system such as damping ratio, natural frequency and static gain of the system are extracted as features from this model. For experimentation purpose a second order Sallen-Key band pass filter and a fourth order Chebychev Type 1 low pass filter is considered, the corresponding fault classes are created for each of the circuit. The parameter values of the passive components in the system have been varied to derive the features, and each component whose tolerance varied is labelled with a corresponding fault class. The proposed methodology classifies faulty classes with accuracy greater than 95%.

A deep network solution for intelligent fault detection in analog circuit

Automatic fault diagnosis in analog electronic circuits is one of the interesting and important cases for researchers of this field that has gained substantial improvements in recent decades. Fault detection issue could be transferred into a classification problem. In this paper, a new fault detection method is proposed based on deep Convolution Neural Network (CNN). We used the real part of Power Spectrum Density (PSD) of faulty signals as the input images of CNN. The main reason for this is extracting microstructure features among signals by using PSD which result in a better discrimination amongst wide range of faults. Our method is evaluated by two benchmark circuits. The superior performance of our method is proved by simulation results and compared with other state of arts.

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

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.

Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame Optimization Algorithm

By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. Moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.

RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits

Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task. Consequently, a technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA). The development of ROA is based on a group of riders, racing toward a target location. Moreover, a classifier, termed RideNN, is developed by including the proposed algorithm as the training algorithm for the neural network (NN). RideNN, along with the orthogonal transformation and Bhattacharyya coefficient, is applied for the fault diagnosis of analog circuits. The proposed technique is experimented using three basic circuits, such as triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC). The performance is evaluated using two evaluation metrics, namely, accuracy (ACC) and false alarm ratio (FAR). The analysis results show that the proposed technique attains an ACC of 99.9% in TWG, 99.9% in BTA, 99% in DIF, and 95% in SPC without noise.

Fault diagnosis of power electronic circuits based on wavelet packet decomposition and LibSVM

A new fault diagnosis method is proposed in this paper for power electronic circuit. An experimental platform of the three-phase full bridge controlled rectifier is built to test the proposed method. The output DC voltage is selected as the original signal to avoid the misdiagnosis because of load change. The features of the original signal are extracted by using the theory of wavelet packet decomposition. The standard deviation of each frequency component of the 3 layer constitutes the feature vector and then the different types of faults are identified by using LibSVM. Satisfied simulation and experiment results are obtained.

Fault Diagnosis of Power Electronic Circuit Based on Hybrid Intelligent Method

Because of the problems of fault diagnosis in the power electric circuit and the merit of FCM is efficient in clustering and the merit of hidden Markov model (HMM) that have the ability to deal with continuous dynamic signals and the merit of support vector machine (SVM) with perfect classifying ability, With the features extracted from the circuit, based on the trained FCM algorithm, HMM was used to calculate the matching degree among the unknown signal and the circuit’s states, which formed the features for SVM to diagnosis. Double-bridge 12-pulse rectifier is used as a example to verifiy the effectiveness of the method. The experimental results show that the proposed method has a good correct rate.

Multi Frequency Approach to Analog Fault Diagnosis using Pole Sensitivity Analysis

In this paper an efficient algorithm using multi-frequency approach has been applied to fault diagnosis of analog electronic circuits using Pole Sensitivity analysis. In this paper, after application of the multi frequency approach to the pole sensitivity analysis the results show that the fault diagnosis of the circuit has increased. This has been proved by taking two circuits a second order Sallen Key Filter and RC Ladder network.

Fuzzy classifier for fault diagnosis in analog electronic circuits

Many studies have presented different approaches for the fault diagnosis with fault models having ±50% variation in the component values in analog electronic circuits. There is still a need of the approaches which provide the fault diagnosis with the variation in the component value below ±50%. A new single and multiple fault diagnosis technique for soft faults in analog electronic circuit using fuzzy classifier has been proposed in this paper. This technique uses the simulation before test (SBT) approach by analyzing the frequency response of the analog circuit under faulty and fault free conditions. Three signature parameters peak gain, frequency and phase associated with peak gain, of the frequency response of the analog circuit are observed and extracted such that they give unique values for faulty and fault free configuration of the circuit. The single and double fault models with the component variations from ±10% to ±50% are considered. The fuzzy classifier along the classification of faults gives the estimated component value under faulty and faultfree conditions. The proposed method is validated using simulated data and the real time data for a benchmark analog circuit. The comparative analysis is also presented for both the validations.

Neural Network based Fault Diagnosis in Analog Electronic Circuit using Polynomial Curve Fitting

studies have been presented for the fault diagnosis of electronic analog circuits with worst case fault models using ±50% variation in the parametric values of the components. The study of for parametric fault detection in electronic analog circuits -faults as small as 10% or less was uncovered. The use of the neural network for parametric fault diagnosis in an analog circuit, based upon the polynomial curve fitting coefficients of the output response of an analog circuit is presented in this study. Building upon the theory of polynomial coefficients we propose a parametric fault diagnosis methodology. A polynomial of suitable degree is fitted to the output frequency response of an analog circuit. The coefficients of the polynomial attain different values under faulty and non faulty conditions. Using these features of polynomial coefficients, a BPNN is used to detect the parametric faults. Simulation results are presented for a benchmark bi quad filter circuit. Single resistance and capacitance faults of ±1% to ±50% deviation from nominal values were correctly diagnosed. KeywordsNetwork, Parametric faults, Analog circuit, Curve fitting, Polynomial coefficients.

Application of Algorithm of Hidden Markov Model and High-Order Spectrum in Fault Diagnosis of Power Electronic Circuit

This paper presents a method of applying the discrete hidden markov model and high-order spectrum to fault diagnosis of power electronic circuit.With bispectrum analysis, an ARMA model parametric bispectrum estimation is presented for Fault feature extraction firstly,and then fault modes were trained and recognized by Discrete Hidden Markov Model. Finally, electric locomotive main converter of SS8 type is used as an example to illustrate the new approach of fault diagnosis. The experimental results show that the proposed method has a high correct rate.The correct rate of the proposed method is 100%in the case of no noise or 5% noise whicn is 16.11% and 23.79% higher respectively than that of DHMM and BP neural network methods. So the method has practical value.

COMPLEX NETWORK APPLICATION IN FAULT DIAGNOSIS OF ANALOG CIRCUITS

Fault diagnosis has played an important role in the identification of fault mechanisms and the subsequent successful isolation of faults in electronic circuits. In this paper, we propose a novel procedure for fault diagnosis in analog circuits. We first generate a set of fault patterns from fault simulation, and our main task is to develop a practical description of the way in which these fault patterns interact. Our approach is based on the construction of a complex network that describes the inter-dependence of the various fault patterns. Analysis of this complex network shows that the degree distribution is scalefree-like and the connectivity is small-world. We henceforth identify a small number of fault patterns that are most highly connected (of highest degrees) with other fault patterns. Furthermore, we study the connection between this network of fault patterns and the original circuit, the purpose being to relate the information of the high-degree fault patterns with the physical circuit topology, thus allowing the physical fault locations and circuit elements to be identified. Our proposed approach will find applications in automatic fault diagnosis of large-scale electronic circuits.

SBT Approach towards Analog Electronic Circuit Fault Diagnosis

An approach for the fault diagnosis of single and multiple faults in linear analog electronic circuits is proposed in this paper. The simulation-before-test (SBT) diagnosis approach proposed in this write up basically consists of obtaining the frequency response of fault free/faulty circuit. The peak frequency and the peak amplitude from the error response are observed and processed suitably to extract distinct signatures for faulty and nonfaulty conditions under maximum tolerance conditions for other network components. The artificial neural network classifiers are then used for the classification of fault. Networks of reasonable dimensions are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances. This is a unique contribution of this paper. Fault computation time is drastically reduced from the traditional analysis techniques. This results in a direct dollar savings at test time. A comparison of the proposed work with the previous works which also employ preprocessing techniques, reveals that our algorithm performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.