An efficient feature extraction approach based on manifold learning for analogue circuits fault diagnosis

Yuan, Zhijie; He, Yigang; Liu, Yuan; Chen, Peng; Cheng, Zhen

doi:10.1007/s10470-018-1377-0

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Huahui Yang et al [14] applied convolutional neural networks in one dimension for diagnosing faults in analog circuits, aiming to simultaneously complete the tasks of extracting relevant features and classifying faults within the input signal through the neural network. Zhijie Yuan [15] used two popular methods in manifold learning methods, local linear embedding (LLE) and diffusion mapping (DM), to optimize the dimensionality reduction techniques commonly used, so as to better extract the fault features in analog circuits.…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

Hu,

Han

et al. 2024

Electronics

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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.

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Section: Introductionmentioning

confidence: 99%

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

Chen,

Hu,

Han

et al. 2024

Electronics

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show abstract

“…Huahui Yang et al [13] applied one-dimensional convo-lutional neural networks to analog circuit fault diagnosis, aiming to simultaneously complete the tasks of feature extraction and fault classification of the input signal through the neural network. Zhijie Yuan [14] used two popular methods in manifold learning methods, local linear embedding (LLE) and diffusion mapping (DM), to optimize the dimensionality reduction techniques commonly used in analog circuit fault diagnosis, so as to better extract the fault features in analog circuits.…”

Section: Introductionmentioning

confidence: 99%

An analog circuit soft fault diagnosis method based on Boruta feature selection and LightGBM model

Chen,

Hu,

Han

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Analog circuits are an important part of modern electronic power systems. Accurate detection of analog circuit faults, especially soft faults, is of great significance for the maintenance and inspection of electronic systems. This paper proposes to apply the Boruta feature selection method to the field of soft fault diagnosis of analog circuits to screen out lowdimensional and efficient feature components from the high-dimensional time-domain statistical features and frequencydomain features of circuit responses. Then, the feature components are used as input to train the LightGBM classification model, and the Bayesian optimization method is introduced to optimize the model hyperparameters. Finally, the trained fault diagnosis model is verified in two typical experimental circuits, and satisfactory accuracy is obtained.

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“…rough fault feature extraction, dimensions of fault feature can be reduced and redundancies of fault feature can be removed. It would be convenient to recognize fault category of analog circuit [14][15][16][17]. It is common to see that signal processing methods, information entropy methods, frequency analysis methods, and time-domain analysis methods are used to extract fault feature in analog circuit fault diagnosis [18].…”

Section: Introductionmentioning

confidence: 99%

Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor

Wang

2021

Scientific Programming

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Analog circuit fault diagnosis is a key problem in theory of circuit networks and has been investigated by many researchers in recent years. An approach based on sparse random projections (SRPs) and K-nearest neighbor (KNN) to the realization of analog circuit soft fault diagnosis has been presented in this paper. The proposed method uses the wavelet packet energy spectrum and sparse random projections to preprocess the time response for feature extraction. Then, the variables of the fault features are constructed, which are used to form the observation sequences of K-nearest neighbor classifier. K-nearest neighbor classifier is used to accomplish the fault diagnosis of analog circuit. In this paper, four-opamp biquad high-pass filter has been used as simulation example to verify the effectiveness of the proposed method. The simulations show that the proposed method offers higher correct fault location rate in analog circuit soft fault diagnosis application as compared with the other methods.

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An efficient feature extraction approach based on manifold learning for analogue circuits fault diagnosis

Cited by 9 publications

References 26 publications

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

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

An analog circuit soft fault diagnosis method based on Boruta feature selection and LightGBM model

Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor

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