BIT-Based Intermittent Fault Diagnosis of Analog Circuits by Improved Deep Forest Classifier

Huang, Congzhi; Shen, Zhendong; Zhang, Jianhua; Hou, Guolian

doi:10.1109/tim.2022.3194890

Cited by 18 publications

(3 citation statements)

References 38 publications

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“…Electrical equipment is widely used in nuclear power, the chemical industry, and other high-security equipment fields, and its working environment is often in high-temperature, high-pressure, high-humidity, and other harsh environments. As the system continues to operate, the reliability of its internal circuit board cards decreases due to its operating environment, which could ultimately lead to unanticipated downtime, increased economic losses, and even production accidents [ 1 , 2 , 3 ]. Furthermore, statistics have shown that although 80% of the board cards are digital, most failures occur in the analog section [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gao,

Guo,

Yuan

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Gao,

Guo,

Yuan

et al. 2024

Sensors

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show abstract

“…Some SBT methods allow a faulty parameter up or down deviation from a nominal value classification or even localization of selected multiple faults. Artificial intelligence methods, including various neural networks, are typically used as fault classifiers in SBT methods [18,28,[30][31][32]39,[47][48][49]. These methods indicate faults of each class with high precision, up to 100%.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Analog Circuits: The Problem of Ambiguity of Test Equation Solutions

Hałgas

2024

Electronics

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Diagnosis of analog electronic circuits is a crucial issue in computer-aided design. During the diagnosis, solving a test equation to identify the values of faulty parameters is usually necessary. The equation is nonlinear to the parameters, even for linear circuits. The nonlinearity of the equation implies the possibility of multiple solutions. No method exists that guarantees the determination of all the solutions of the test equation. However, even information about more than one existing solution is essential for the designer. It allows for the selection of another test at the design step and helps to obtain an unambiguous solution during the diagnosis. Information about the possibility of additional solutions is essential for simulation after test methods (e.g., identification and verification methods) and for simulation before test methods, so-called dictionary methods, especially those targeting multiple fault classification. The paper deals with the problem of multiple solutions of the test equation for nonlinear DC circuits and proposes a method for identifying the solutions using a deflation technique. The outcomes are compared with the results obtained using standard and adaptively damped Newton–Raphson iterative methods. The methods use randomly selected initial guesses to find multiple solutions. The effectiveness of all the methods for identifying multiple solutions was verified numerically and via laboratory tests.

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“…Deep forest is a deep cascade learning model proposed by Zhou [5] in 2017. It not only has the advantages of few parameter settings and simple structure, but also Huang [6] and others have proved that deep forest also has high diagnostic accuracy for small sample data.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of intelligent electric meter based on SCGWO-DF

Shen

Zhang

Yuan

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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The diagnosis accuracy of smart meters is very low due to the uneven distribution of fault data. In order to improve the fault diagnosis accuracy of smart meters, a fault diagnosis method for smart meters based on the fusion of improved gray wolf algorithm and deep forest classifier (SCGWO-DF) is proposed. Firstly, the daily operation data of intelligent ammeter is obtained, and the data is classified according to the fault type, and the training set and test set are divided. Secondly, the training set is put into the deep forest classifier to train the diagnostic model. Thirdly, the improved grey wolf optimization algorithm is used to optimize the three key parameters: the number of features, the number of random forests and the number of completely random forests. Finally, the trained model is verified by using the data of a power plant smart meter. The experimental results show that the SCGWO-DF diagnostic method proposed in this paper has a higher accuracy rate than the traditional SVM, DBN and random forest methods, and the accuracy rate reaches 98%.

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BIT-Based Intermittent Fault Diagnosis of Analog Circuits by Improved Deep Forest Classifier

Cited by 18 publications

References 38 publications

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

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

Diagnosis of Analog Circuits: The Problem of Ambiguity of Test Equation Solutions

Fault diagnosis of intelligent electric meter based on SCGWO-DF

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