Analog Circuit Fault Diagnosis Research Articles

Soft fault diagnosis of analog circuits based on semi-supervised support vector machine

Soft fault diagnosis has been validated as a very challenging problem in analog circuits. In order to improve the generalization ability and close to the practical application of fault diagnosis models, a novel method for obtaining a large number of training samples in soft fault interval is proposed in the present work. Training samples are randomly generated in the interval of soft fault to adapt the continuously change of component parameters. Limits of experimental conditions, lead to the limited number of samples labeled by experts, so that training samples are classified using the semi-supervised support vector machine (S3VM) algorithm. Manifold learning algorithm has been used for the feature extraction and the dimension reduction of soft fault time domain response data in analog circuits. Then the semi-supervised Gaussian mixture model (SGMM) is applied to cluster decision trees. Finally, the S3VM classification is used for the soft fault diagnosis in analog circuits. Experimental results show that the proposed method can be utilized in the single and double soft fault diagnosis of analog circuits. It is observed that the S3VM method is extremely significant as a guide in actual engineering applications, although the diagnosis rate is slightly less than the fixed-parameter offset soft faults.

An end-to-end denoising autoencoder-based deep neural network approach for fault diagnosis of analog circuit

Fault diagnosis of analog circuit is critical to improve safety and reliability in electrical systems and reduce losses. Traditional fault diagnosis methods of analog circuit usually rely on the hand design feature extractor and can not generalize well to other diagnosis domains. To address these issues, an end-to-end denoising autoencoder (EEDAE)-based fault diagnosis approach is proposed. The proposed approach includes denoising autoencoder (DAE) and a softmax classifier. The DAE is designed to automatically extract fault features from the raw time series signals without any signal processing techniques and diagnostic expertise, and then the softmax classifier is used to classify the fault mode of analog circuits. Specifically, we design a novel loss function by jointly minimizing reconstruction loss and classification loss to improve training efficiency. The proposed approach just has one training stage, in which the encoder, decoder, and classifier are trained simultaneously. The experimental results demonstrate that compared with traditional methods, the proposed method has higher accuracy and lower requirements on data.

Application of DBN and GWO-SVM in analog circuit fault diagnosis

For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

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

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.

The intermittent fault diagnosis of analog circuits based on EEMD-DBN

Aiming at the intermittent faults widely existing in analog circuits, caused by poor soldering and performance degradation of components, a method for the intermittent fault diagnosis of analog circuits based on ensemble empirical mode decomposition (EEMD) and deep belief network (DBN) is proposed. The amplitude and frequency anomaly signals in the frequency domain is regarded as triggering signals for intermittent fault detection, so the signal segments containing intermittent faults can be detected. The signal segment with intermittent fault is decomposed into multiple intrinsic mode functions (IMFs) by EEMD, and the IMF and fault features are optimized by using Pearson correlation coefficient and feature separability. The optimization feature set is established, which is used as feature vector to transmitted to DBN for fault diagnosis. The proposed method can autonomously realize the feature selection and the diagnosis of intermittent fault. The optimization features make DBN improve diagnostic accuracy, reduce diagnostic time, and can locate intermittent faults to the circuit branch with intermittent faults. The simulation experiments show that the proposed method has strong fault diagnosis capability. And the comparative experiments prove that compared with other commonly used methods, the EEMD-DBN method has higher diagnostic accuracy in intermittent fault diagnosis of analog circuits.

Fault Diagnosis of Analog Circuits Based on Wavelet Packet Energy Entropy and DBN

To solve the problem of low classification and recognition rate caused by small number of fault samples and inaccurate feature extraction in analog circuit fault diagnosis, a fault diagnosis algorithm for analog circuit system based on the combination of wavelet packet energy entropy and Deep Belief Network (DBN) is proposed. Firstly, the original output voltage signal of the circuit is decomposed by a multi-layer wavelet packet, then the feature vector is constructed in the form of energy entropy, and then the principal component analysis (PCA) is used for feature selection. The reduced dimension feature vector is taken as the input vector of DBN, and the fault diagnosis is completed after training and learning of the DBN network model. The experimental results show that compared with other algorithms, the proposed method can be more accurate and effective in diagnosing fault types in analog circuits, especially for Sallen-Key band-pass filter circuits, the fault recognition rate reaches 100%.

A Modified Sine Cosine Algorithm With Teacher Supervision Learning for Global Optimization

The sine cosine algorithm (SCA) is a recently developed meta-heuristic algorithm for solving global optimization problems. It has shown excellent performance in meta-heuristic algorithms. But this algorithm also has shortcomings such as low accuracy, easy to fall into a local solution, and slow convergence speed. Aiming at these deficiencies of the SCA, a modified sine cosine algorithm with teacher supervision learning (TSL-SCA) for global optimization is proposed. First, the teacher supervision strategy can guide the population convergence and accelerate the convergence speed. Second, individuals perform reflective learning after the standard SCA position is updated, which can effectively prevent individuals from stagnating in the evolutionary process and increase population diversity. In addition, a hybrid inverse learning method is proposed. It can not only enhance the ability of finding global optimal solution and increase population distributivity, but also balance the exploration and exploitation capabilities. Differential evolution algorithm (DE), particle swarm optimization (PSO), cuckoo search (CS) algorithm, moth-flame optimization (MFO), whale optimization algorithm (WOA), Teaching-Learning-Based Optimization (TLBO), SCA and TSL-SCA are selected for simulation experiment to solve 33 benchmark optimization problems. The experimental results show that the TSL-SCA can significantly enhance the optimization accuracy and convergence speed. Furthermore, the effectiveness of the proposed method is examined by solving analog circuit fault diagnosis of filter circuit examples.

Fault Diagnosis of an Analog Circuit Based on Hierarchical DVS

Analog circuit fault diagnosis technology is widely used in the diagnosis of various electronic devices. The basic strategy is to extract circuit fault characteristics and then to use a clustering algorithm for diagnosis. The discrete Volterra series (DVS) is a common feature extraction method; however, it is difficult to calculate its parameters. To solve the problem of feature extraction in fault diagnosis, we propose an improved hierarchical Levenberg–Marquardt (LM)–DVS algorithm (IDVS). First, the DVS is simplified on the basis of the hierarchical symmetry of the memory parameters, the LM strategy is used to optimize the coefficients, and a Bayesian information criterion based on the symmetry of entropy is introduced for order selection. Finally, we propose a fault diagnosis method by combining the improved DVS algorithm and a condensed nearest neighbor algorithm (CNN) (i.e., the IDVS–CNN method). A simulation experiment was conducted to verify the feature extraction and fault diagnosis ability of the IDVS–CNN. The results show that the proposed method outperforms conventional methods in terms of the macro and micro F1 scores (0.903 and 0.894, respectively), which is conducive to the efficient application of fault diagnosis. In conclusion, the improved method in this study is helpful to simplify the calculation of the DVS parameters of circuit faults in analog electronic systems, and provides new insights for the prospective application of circuit fault diagnosis, system modeling, and pattern recognition.

An adaptive new state recognition method based on density peak clustering and voting probabilistic neural network

The traditional fault diagnosis method based on classifiers relies heavily on complete prior knowledge of the existing faults and often requires complex denoising calculations. However, in practical applications, it is difficult to obtain the complete fault state data, especially degradation state data, in advance. This reduces the classification accuracy of unknown new states and severely limits fault diagnosis. To overcome these difficulties, a novel analog circuit fault diagnosis and unknown state recognition method based on density peak clustering and voting probabilistic neural network (VPNN) is presented. In this method, the novel VPNN model is constructed based on prior knowledge of faults, in which a majority voting algorithm is applied instead of the complex process of denoising, to restrain the environmental noise. Further, a newly designed discriminant layer is added after the summation layer to identify the unknown state data, pursuant to which a derivative-based method is proposed to eliminate the transition data. Moreover, to update the preliminary VPNN, the K-nearest neighbor and density peak clustering procedures are applied to automatically determine the number of new pattern neuron classes, and a data reduction algorithm based on the Gaussian mixture model is proposed to determine the pattern neuron samples. Accordingly, numerous redundant samples are reduced. Thus, new pattern neurons can be straightforwardly added to the preliminary VPNN. Our experimental results clearly demonstrate the robustness of the proposed method, which can reduce false alarms, identify unknown states, and determine the newly added pattern neuron classes and pattern neuron samples to update the VPNN automatically.

Fault Diagnosis of Nonlinear Analog Circuit Based on Generalized Frequency Response Function and LSSVM Classifier Fusion

For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

Fault Diagnosis Method of Analog Circuit Based on Radial Base Function Neural Network

Neural network is competent for fault diagnosis and pattern classification such as poorly defined model system, noisy input environment and nonlinearity in analog circuit. One of the most known type of neural network used to identify and classify the method of faulty diagnosis of analog circuit is presented based on the radial basis function (RBF) neural network. The proposed method introduces the fault features and classify the fault classes of the given two circuits. The experiment and simulations of the fault diagnosis for the two circuits (Sallen-Key and four OP-AMP band-pass filter) establish the diagnosing process of our method that proposed in this paper, locating faults effectively and be successful in classifying the fault values. Its performance analysis show that the fault diagnosability learned by RBF method accomplishes a better satisfactory diagnostic accurateness for the fault diagnosis of analog circuit.

Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis

The whale optimization algorithm (WOA) is a new swarm intelligence (SI) optimization algorithm, which has the superiorities of fewer parameters and stronger searching ability. However, previous studies have indicated that there are shortages in maintaining diversity and avoiding local optimal solutions. This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies. First, the chaotic initialization strategy is performed to enhance the quality of the initial population. Then, an improved random searching mechanism is designed to reduce blindness in the exploration phase and speed up the convergence. In addition, the original spiral updating position is modified by the Levy flight strategy, which leads to a better tradeoff between local and global search. Finally, an enhanced position revising mechanism is utilized to improve the exploration further. To testify the superiorities of the proposed MSWOA algorithm, a series of comparative experiments are carried out. On the one hand, the numerical optimization experimental results, which are conducted under nineteen widely used benchmark functions, indicate that the performance of MSWOA stands out compared with the standard WOA and six other well-designed SI algorithms. On the other hand, MSWOA is utilized to tune the parameters of the support vector machine (SVM), which is applied to the fault diagnosis of analog circuits. Experimental results confirm that the proposed method has higher diagnosis accuracy than other competitors. Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.

Combined Optimization of Neural Network Fault Diagnosis Methods for Analog Circuits on Ships

Guo, S.; Li, H.; Wu, B.; Zhou, J.; Su, C., and Guan, C., 2020. Combined optimization of neural network fault diagnosis methods for analog circuits on ships. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 158–164. Coconut Creek (Florida), ISSN 0749-0208.With increasing automation, electronic systems are becoming more widely used on ships. When electronic systems fail, it will greatly affect the safety of ship operation or even cause catastrophic accidents. Therefore, it is necessary to diagnose a circuit fault in order to repair the equipment in time. In electronic systems, more than 80% of the faults come from analog circuits. To obtain better fault diagnosis accuracy for analog circuits, neural network classification algorithms are often combined with optimization steps, such as circuit feature collection, feature engineering, regularization and so on, significantly affecting the accuracy of the classification algorithm, such as circuit feature collection, feature engineering, and regularization. However, the accuracy of analog circuit fault diagnosis is often not high in practical applications because of the tolerance of analog circuit components and other factors. To solve the above problems, this paper proposes a neural network combination optimization method based on hypothesis testing and applies it this method to the fault diagnosis of analog circuits on ships. This method uses hypothesis testing to combine and sort the optimization methods of each step in a neural network classification algorithm to obtain a model ranking based on the accuracy rate with mathematical statistics rules, thereby obtaining the optimal combination method. Finally, the validity of the method is verified by experiments.

Analog circuit fault diagnosis based on density peaks clustering and dynamic weight probabilistic neural network

Fault diagnosis methods based on probabilistic neural networks (PNNs) have been widely used in various products, owing to their simplicity and efficiency. However, in some multi-condition circuit fault diagnoses, the presence of numerous faults and heterogeneity of the training data make the computational efficiency, classification accuracy, and selection of the pattern neuron samples of the PNN challenging. To overcome these difficulties, this study proposes a novel analog circuit fault diagnosis method based on density peaks clustering and a dynamic weight PNN. In this method, density peaks clustering is performed to determine the number of pattern neuron classes. Based on the results of clustering, a priority function is defined, and a novel two-step pattern neuron optimization algorithm incorporating local density and gravity is proposed. Accordingly, the representative boundary data are selected as the pattern neuron samples and numerous redundant samples are reduced. Moreover, a dynamic grey correlation weight determination (GCWD) algorithm between the input and pattern layers is proposed, to determine which classes of the pattern neurons need to be activated and involved in the diagnostic calculation. In addition, a dynamic proportion-amplified weight determination (PAWD) algorithm between the pattern and summation layers is suggested to reduce the adverse effect of the heterogeneity. This not only reduces the number of calculations in the diagnostic process, but also improves the accuracy of the diagnostic model. Case study on a filter circuit clearly demonstrates that the proposed method can achieve high classification accuracy with only a few pattern neurons.

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

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.

Research on Fault Diagnosis of Analog Circuit Based on Volterra Theory and Higher-Order Spectrum Analysis

To diagnose fault of nonlinear components in analog circuits, a method of fault diagnosis based on Volterra series theory, high-order spectrum and kernel assurance criterion (KAC) analysis was proposed. Firstly, input and output signals of the analog circuit were used to identify a Volterrra model. Secondly, the kernel function was solved by the improved multi-pulse excitation method. The high-order spectrum and its slice method were used to qualitatively characterize the kernel function. Finally, the concept of KAC indicator was introduced to quantitatively compare the characteristics of soft faults in the analog circuit, and the fault degree information was extracted and diagnosed. Taking the Sallen-key filter circuit as the research object, the simulation of the method shows that the high-order spectrum and KAC index can be used to quantify the Volterra system with different fault levels, which can effectively distinguish analog circuit with different soft faults, and have certain practical significance.

Generative Adversarial Networks With Comprehensive Wavelet Feature for Fault Diagnosis of Analog Circuits

Analog circuits are one of the most commonly used components in industrial equipment. Circuit failure may lead to significant causalities and even enormous financial losses. To cope with this problem, a novel deep learning strategy based on the cross-wavelet transform (XWT) and generative adversarial networks (GANs) is proposed for analog-circuit fault diagnosis. Different from the traditional methods that simply apply the amplitude characteristic on the time–frequency plane, XWT is applied to obtain the spectral images of the fault signals that can be used to capture the comprehensive features with the involvement of the amplitude, phase, and coherence characteristics. Furthermore, a data-regularization trick is employed on these spectral images to construct three-channel feature images data. The input data can reflect the complex relationship effectively between the measured signals and the feature space, with abundant information of multiple domains on the time–frequency plane. Finally, the different distributions for various fault conditions can be learned by the proposed GAN. The adversarial training manner of the generative network and the discriminative network of the GAN can not only mine the complex nonlinear relationship between the root cause of the failure and the signal signature but also alleviate the problem of overfitting that is caused by the limited availability of the training samples. Furthermore, we expand the GAN to a supervised classifier with the coordinative usage of two auxiliary classifiers, and two convolution neural networks are adopted to establish the basic structure of the proposed model. The comparisons of classification performance with other methods indicate that the proposed approach has good potential for analog-circuit fault diagnosis.

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits.

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

The present study applies the one-dimensional convolutional neural network (1D-CNN) to propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The raw signals based on various soft faults from the output terminal of the circuit under test (CUT) are collected with appropriate data acquisition system to implement a data-driven fault diagnosis. The data-driven diagnosis process is typically encapsulated in two distinct blocks, including the feature extraction and the classification. In this study, the designed 1D-CNN model efficiently combines the aforementioned two phases into a single diagnosis body with fast learning rate and accurate classification. The main advantages of the 1D-CNN are: 1) it can be directly established to the raw signal with proper training so that it is more applicable in real applications; 2) its compact architecture and configuration has reasonable applicability in complex analog circuits; 3) convolutional kernels guarantee that the hierarchical features can be extracted from raw data with better anti-interference performance. Moreover, since the method can extract high-level features of raw signals, it resolves the necessity to employ other per-processing methods for the hand-crafted feature transformation. The performance of the proposed 1D-CNN model is evaluated through three benchmark circuits on the SIMULINK platform. Obtained results are compared with other intelligent fault diagnosis methods. The experimental results show that the 1D-CNN can be utilized effectively as the feature exactor and faults classifier for analog circuits.

Soft fault diagnosis of linear circuits with the special attention paid to the circuits containing current conveyors

This paper deals with soft fault diagnosis of linear analog circuits and concentrates on the circuits including second generation current conveyors. Properties of these circuits are taken into account and an appropriate realistic conveyor model is put forward. The fault diagnosis includes fault detection, locating faulty elements from among all circuit elements, and estimating their values. The diagnostic method developed in this paper exploits a measurement test in an AC state and uses the nonlinear programming as a mathematical tool. Values of the faulty parameters may belong to wide ranges around their nominal values. For illustration a real-life current conveyor circuit is laboratorily and numerically tested. Proposed method efficiently diagnoses different faults giving correct results in short time. Although the method is dedicated to single fault diagnosis, it can be generalized to double and triple faults either. However, in the case of multiple faults it is less effective.