Analogue circuit fault diagnosis based on convolution neural network

Du, Tao; Zhang, Hao

doi:10.1049/el.2019.2892

Cited by 24 publications

(11 citation statements)

References 5 publications

(5 reference statements)

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“…The t-SNE technology is a non-linear unsupervised dimensionality reduction method commonly used in the field of deep learning. This method can visualize high-dimensional features in a two-dimensional space to intuitively judge whether the high-dimensional features are discriminative [17,18]. This section uses t-SNE technology to visualize the original fault data of the two benchmark test circuits and the high-dimensional features extracted from the last layer of the BPNN, RNN, LSTM, 1DCNN and MSCNN-SK models in two dimensions.…”

Section: Feature Visualization Analysismentioning

confidence: 99%

“…Yang et al [16] proposed a one-dimensional convolutional neural network (1DCNN) to conduct analog circuit fault diagnosis, which used raw signals as the input. Du et al [17] developed a CNN-based approach for analog circuit fault diagnosis, and the output signals in different fault states are directly input into CNN. However, these studies still have the following two weaknesses:…”

Section: Introductionmentioning

confidence: 99%

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Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

2021

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Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.

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Section: Feature Visualization Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

2021

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“…erefore, the task of classifying the real data is extracted by the fault diagnosis model based on convolution neural network, and the discriminator only needs to identify the generated data and the real data [17,18]. e disadvantage of convolution neural network discussed before is that it is a supervised learning method.…”

Section: Generate a Fault Diagnosis Model Combining Countermeasure Network With Convolution Neural Networkmentioning

confidence: 99%

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

Yi¹

2021

Mobile Information Systems

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With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

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“…Feature extraction plays an important role in fault diagnosis, and the powerful feature extraction capability of deep learning makes it highly popular among experts and scholars [12]. Classical deep learning methods for fault diagnosis include autoencoder, convolutional neural network (CNN), deep neural network (DNN), and so on [13][14][15][16][17][18][19][20]. Combining deep learning with transfer learning in fault diagnosis is a new research direction [21].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Yang,

Li,

Wen

et al. 2023

Meas. Sci. Technol.

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In this paper, we propose a one-dimensional convolutional neural network(1D-CNN) model based on the adaptive batch normalization (AdaBN) algorithm to improve the CNN model, which is difficult to extract features from multi-rotor unmanned aerial vehicle (UAV) rotor structural faults under variable conditions and has poor fault diagnosis performance. The method accomplishes fault diagnosis and classification by feature extraction from lower dimensional multi-rotor UAV data. The AdaBN algorithm adjusts the parameters of the BN layer in the model during the testing phase to improve the domain adaptive capability of the model in scenarios with variable operating conditions. Also, to improve the robustness of the model under noisy conditions, the first layer of convolutional kernel dropout operation is introduced to improve the noise immunity of the model. To reduce the complexity of manual tuning and to find the optimal combination of hyperparameters for the network model more effectively, the grey wolf optimizer algorithm is used to optimize the hyperparameters and further improve the model performance. Finally, the effectiveness of the proposed method is verified through comparison tests, and it shows good diagnostic effects in noise and variable conditions compared with several commonly used fault diagnosis methods.

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Analogue circuit fault diagnosis based on convolution neural network

Cited by 24 publications

References 5 publications

Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

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