Analog Circuit Fault Diagnosis Research Articles

A novel analog circuit fault diagnosis method based on multi-channel 1D-resnet and wavelet packet transform

A novel analog circuit fault diagnosis method based on multi-channel 1D-resnet and wavelet packet transform

An adaptive deep belief network for fault diagnosis of complex analog circuits

An adaptive deep belief network for fault diagnosis of complex analog circuits

A Complex Analog Circuit Fault Diagnosis Method based on IMODA Improved Deep Belief Network

Aiming at the problems of time-consuming and low diagnosis accuracy during the unsupervised training process of traditional DBN, an analog circuit fault diagnosis method based on Improved Multi-Objective Dragonfly Optimized Deep Belief Network (IMODA-ADBN) is proposed. The method employs an improved MODA algorithm instead of the BP algorithm, which improves the classification accuracy of the network and ameliorates the problem of being prone to falling into local optima. The algorithm is tested on three multi-objective mathematical benchmark problems and compared with three well-known meta-heuristic optimization algorithms such as MODA, MOPSO and NSGA-II, and the results demonstrate the stability of the IMODA-ADBN network model. Finally, IMODA-ADBN is applied to the diagnostic experiments of a two-stage quad op-amp dual second-order low-pass filter, and the results show that the method improves the classification accuracy and diagnostic rate while guaranteeing the convergence speed, and is able to effectively realize the classification and localization of difficult faults.

An analog circuit fault diagnosis method using improved sparrow search algorithm and support vector machine.

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy.

A Method of Diagnosing Analog Circuit Soft Faults Using Boruta Features and LightGBM

Modern electronic power systems rely heavily on analog circuits. The accurate detection of analog circuit faults, especially soft faults, is of great significance for the maintenance and inspection of electronic systems. This paper proposes the application of the Boruta feature selection method to the field of the soft fault diagnosis of analog circuits to screen out low-dimensional and efficient feature components from the high-dimensional time-domain statistical features and frequency-domain features of circuit responses. Then, the feature components are used as the input to train the LightGBM classification model, and the Bayesian optimization method is introduced to optimize the model’s hyperparameters. Finally, the trained fault diagnosis model is verified in two typical experimental circuits, and satisfactory accuracy is obtained.

Research on the mainstream analog circuit fault diagnosis methods

Electronic products have been an indispensable part of people's daily life, in which the circuit as the basis of electronic products plays a very important role. However, when the circuit is damaged, the cause is very complicated and difficult to judge directly, so the fault diagnosis of the circuit is very important. In the past five years, this paper has studied circuit fault diagnosis methods, most of which are based on analog circuits. In recent years, the mode of building a platform based on artificial intelligence algorithms and joint software has become relatively mature. This paper summarizes and discusses the research on algorithms, artificial intelligence, and software. According to the discussion, it is found that the various researches on this model have made great progress in the past five years, and the direction of analog circuit diagnosis will have more practical opportunities in the future.

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism.

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time-frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time-frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.

Fault diagnosis of analog circuit based on wavelet transform and neural network

Fault diagnosis of analog circuit based on wavelet transform and neural network

WavePHMNet: A comprehensive diagnosis and prognosis approach for analog circuits

Even though analog circuits comprise a small minority among all circuits, they are responsible for the vast majority of all faults. Hence, analog circuit fault diagnosis and prognosis is crucial in preventing failure and reducing unplanned downtime in industrial electronics. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with circuit complexity. This paper presents an advanced design of experiments-based approach, using supersaturated and space-filling designs, to account for components that degrade in both an individual and interacting fashion, to narrow down the number of possible fault classes that must be considered. Next, a wavelet-based deep learning network called WavePHMNet is developed that can localize the circuit component(s) that is the source of degradation and estimate the value of the degraded component(s), all based solely on the output waveforms produced by the circuit. This degraded value can be used in conjunction with component degradation models to predict circuit remaining useful life. An implementation of this approach is demonstrated on three circuits: a Sallen-Key bandpass filter (7 components), a two-switch forward convertor (25 components), and a digital to analog convertor (260 components). The approach is also demonstrated experimentally on the two-switch forward convertor circuit.

Application of WCA-RBF Neural Network in Fault Diagnosis of Analog Circuits

With the fast growth of electronic technology today, the hybrid circuit of analog and digital circuits has become a trend in the growth of electronic technology. To address fault diagnosis in analog circuits, Haar wavelet is applied for fault feature extraction. The K-means clustering method and pseudoinverse algorithm were used to optimize the center value and weight value of the radial basis function neural network, respectively. The adaptive step size was improved in the wolf pack algorithm, the parameters of the radial basis function neural network were optimized based on the wolf pack algorithm, and a wolf pack algorithm optimized radial basis function neural network model was constructed. The test results show that this model converges after 40 times of training, with an error value of 10−3 and an average value of the mean squared error of 0.45. Comparing the fault diagnosis rates of the original model, genetic algorithm optimized radial basis function neural network model, and wolf colony algorithm optimized radial basis function neural network model, the last model has the best fault diagnosis rate, reaching 95.52%. The wolf colony algorithm is utilized to optimize the radial basis function neural network model to diagnose the faults in the standard filter circuit, and the fault diagnosis rate reaches 96.17%. The findings express that the radial basis function neural network model optimized by the wolf colony algorithm has a good diagnosis effect for different faults in the analog circuit.

Fault diagnosis of analogue circuits based on artificial intelligence algorithms

Abstract The fault diagnosis problem of analog circuits has been paid more and more attention, and the realization of circuit fault feature extraction and pattern classification are two of the key problems. This paper first combines wavelet packet and energy entropy to design a fault feature extraction method based on wavelet packet entropy to solve the problem of analog circuit fault feature extraction. Secondly, the principle of SVM and particle swarm optimization algorithm are combined to design the fault diagnosis process of analog circuits based on an artificial intelligence algorithm. Finally, take the sallen-key circuit as an example to analyze the effectiveness of the wavelet packet entropy algorithm for fault feature extraction and analyze the effectiveness of this paper’s method based on the analog circuit of Wen’s bridge oscillation circuit. The results show that after wavelet packet entropy extraction of the feature vector values is less than 0.001, and the accuracy of the extraction is more than 0.98, the best parameters of the optimization is (182.4, 0.05), the false alarm rate of the fault diagnostic method is 0, the misdiagnosis rate is 0.08, the omission rate is 0, and the correctness rate of the diagnosis of the fault is 0.92. Based on this research is able to carry out the fault diagnosis of the analog circuit.

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models.

As a pivotal integral component within electronic systems, analog circuits are of paramount importance for the timely detection and precise diagnosis of their faults. However, the objective reality of limited fault samples in operational devices with analog circuitry poses challenges to the direct applicability of existing diagnostic methods. This study proposes an innovative approach for fault diagnosis in analog circuits by integrating deep convolutional generative adversarial networks (DCGANs) with the Transformer architecture, addressing the problem of insufficient fault samples affecting diagnostic performance. Firstly, the employment of the continuous wavelet transform in combination with Morlet wavelet basis functions serves as a means to derive time-frequency images, enhancing fault feature recognition while converting time-domain signals into time-frequency representations. Furthermore, the augmentation of datasets utilizing deep convolutional GANs is employed to generate synthetic time-frequency signals from existing fault data. The Transformer-based fault diagnosis model was trained using a mixture of original signals and generated signals, and the model was subsequently tested. Through experiments involving single and multiple fault scenarios in three simulated circuits, a comparative analysis of the proposed approach was conducted with a number of established benchmark methods, and its effectiveness in various scenarios was evaluated. In addition, the ability of the proposed fault diagnosis technique was investigated in the presence of limited fault data samples. The outcome reveals that the proposed diagnostic method exhibits a consistently high overall accuracy of over 96% in diverse test scenarios. Moreover, it delivers satisfactory performance even when real sample sizes are as small as 150 instances in various fault categories.

Fault Diagnosis of Analog Circuits Based on The Sailfish Algorithm Optimized SKELM

In view of the long training time for the optimization of the network model parameters of the SELM and the uncertainty of the model generalization ability, this paper proposes an analog circuit fault diagnosis model based on the sailfish algorithm to optimize the stacked kernel extreme learning machine(SKELM). This model introduces a kernel function to build a multi-layer KELM, which can improve the generalization ability and learning speed of the feedforward neural network. The weights of each layer of SKELM are obtained through the automatic encoder training based on the KELM. Since KELM-AE does not need to set initial parameters, the training speed is improved. However, the kernel parameters and regularization coefficients of KELM-AE are set manually, so the sailfish optimizer (SFO) is used to optimize these two parameters, and then the optimal SKELM model is built through layer by layer training. Finally, the Leap frog filter circuit is used as the simulation experiment circuit, and further compared with the optimized SELM. The results show that KELM-AE has strong generalization ability, and it can map fault features to high-dimensional feature space through nonlinear mapping without extracting fault features separately, thus improving the classification accuracy.

Intermittent fault diagnosis of analog circuit based on enhanced one-dimensional vision transformer and transfer learning strategy

As the major cause of false alarms in built-in test (BIT) system, intermittent faults of analog circuits may trigger abnormal equipment shutdown and lead to catastrophic accidents. With complete randomness and great non-repeatability, intermittent faults are arduous to be detected. To enhance the reliability and safety of electronic systems, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is proposed to realize intelligent diagnosis for intermittent faults of analog circuits. The signal anomaly caused by intermittent faults can be regarded as a kind of random anomaly from global perspective, and there are also rich local feature information in the fault interval. Completely composed of self-attention mechanism, Vision Transformer possesses prominent performance on extracting global features and modelling global representations, thus can be applied to identify intermittent faults. Meanwhile, to further enrich the feature representation, one multi-scale convolution fusion module (MSC) incorporating a series of convolution operations is designed and combined with 1DViT to extract and fuse the valuable local information. However, in practical test, due to the complex operation process, it is cumbersome to collect sufficient fault data to guarantee the effective training of the proposed model. To cope with this problem, transfer learning strategy is introduced. The model will be first pre-trained with adequate simulation data which is easily accessible, and then fine-tuned with a relatively small amount of actual fault data to help match the practical feature distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test.

Fault Diagnosis of Support Vector Machine Analog Circuits Based on Improved Particle Swarm Optimization

The development of electronic circuits requires that the reliability and security of circuit equipment and system operation are also increasing. In addition, due to the complexity of the operating environment, it is very important to strengthen the fault diagnosis and real-time testing technology of analog circuits in circuit systems. Based on this, this paper studied the fault diagnosis of analog circuits with Support Vector Machine (SVM), and introduced Improved Particle Swarm Optimization (IPSO) algorithm to optimize the parameters of SVM. In other words, the dynamic weight setting and factor improvement of Particle Swarm Optimization (PSO) algorithm aim to accelerate algorithm performance improvement, and information extraction and diagnosis model construction are carried out on the basis of considering circuit fault characteristics. Through the performance test and application analysis of the improved algorithm proposed in the study, the error value of the improved algorithm was basically stable at 0.0103 in the late stage of classification training, and its prediction accuracy rate was more than 80%, and the classification consumption time was less. At the same time, the accuracy of fault feature extraction results in training and test scenarios was above 94%, and the search performance was obviously better than other comparison algorithms, which effectively improved the fault diagnosis accuracy and efficiency. The IPSO algorithm model can effectively identify analog circuit fault information, and shows good information optimization performance. It has certain validity and rationality in circuit fault diagnosis and security assurance.

Analog Circuit Fault Diagnosis Based on the Fractional Sliding Model Observer

Analog Circuit Fault Diagnosis Based on the Fractional Sliding Model Observer

Analog circuit fault diagnosis based on RBF

Analog circuit fault diagnosis is growing common using techniques based on artificial intelligence (AI). Among these, defect diagnosis based on radial basis function (RBF) has recently received attention and has demonstrated a respectable level of accuracy. This paper's goal is to demonstrate this method's thought process, methodology, and specific actions. Researchers can refer to the reference circuit diagram, experimental table, and analytic process in the publication to evaluate the efficacy of this approach. Additionally, the elements that can be changed and improved upon are highlighted, offering a path for future study.

A research agenda of AI-based analog circuit fault diagnosis with bibliometric analysis

Analog circuit fault identification is crucial for preserving regular operation. Methods based on artificial intelligence (AI) provide excellent accuracy in fault identification. AI-based techniques have been used widely in recent years, displaying remarkable variety and complexity. Therefore, in order to have a clearer understanding of the issues in this area, it is necessary to summarise and classify the techniques. In this article, various AI-based fault detection techniques are displayed, and bibliometrics is used to show the trend and citations. The relationships, significant writers, and journals will first be discussed in this article, after which some key pieces of literature will be displayed. The key findings and conclusions, clarification of the current issues, and a summary of the present and foreseeable research trends are all included in the last section.

Strategy for Identifying Analog Circuit Faults Using Improved Neural Network Algorithms

<p>Analog circuit faults are the main cause of performance degradation or paralysis in integrated circuit systems. However, due to the complex causes and diverse manifestations of circuit faults themselves, traditional methods have high difficulty in identifying typical faults in analog circuits and low recognition accuracy. This article constructs an improved ResNet deep feature recognition network model and establishes one-dimensional and two-dimensional fault information sources. Finally, particle swarm optimization algorithm is used to search for the optimal parameters solved by the model, ultimately achieving improvements in the accuracy and recognition speed of analog circuit fault diagnosis. Finally, through experimental verification, the recognition accuracy of typical fault C2 reached 99.6%, proving the effectiveness of the method proposed in this paper.</p> <p> </p>