Nonlinear Dimension Reduction on Analog Circuit Fault Diagnosis Using L-Isomap

Wang, Yuehai; Ma, Yuying; Cui, Shiming; Yan, Yongzheng; Li, Xiong

doi:10.12783/dtcse/cnai2018/24138

Cited by 1 publication

(2 citation statements)

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“…These variations can arise from material characteristics, manufacturing processes, and environmental conditions. In practical applications, the tolerance of circuit components such as resistors is typically 5%, and for capacitors, it is 10% [11][12][13][14][15]. The parameter values of components randomly vary within a tolerance range and approximately follow a normal distribution.…”

Section: B Tolerance Of Circuit Componentsmentioning

confidence: 99%

“…However, with the increase in the number of circuit components and the improvement of integration level, accurate mathematical models for analogue circuits become challenging to obtain, limiting their practical application [10]. Therefore, in recent years, data-driven approaches have been increasingly applied in analog circuit fault diagnosis [11][12][13]. Typically, datadriven methods involve two steps: fault feature extraction and fault classification.…”

Section: Introductionmentioning

confidence: 99%

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Analog Circuit Incipient Fault Detection Based on Attention Mechanism and Fully Convolutional Network

Miao,

Zhang,

Chen

et al. 2024

IEEE Access

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Owing to the tolerance of circuit components in analogue circuits, feature overlap occurs between different component faults, especially in the case of early-stage faults. To address this issue, we present an improved model that utilizes the channel attention mechanism in conjunction with a fully convolutional network. The proposed model conducts end-to-end diagnosis by utilizing the response signals generated from the tested circuit. To diagnose different fault categories, we add a global average pooling layer to the last layer of the fully convolutional network to obtain the probabilities of various fault categories. Furthermore, we incorporating a channel attention, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones, within the network, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones. The proposed method is validated with four-opamp biquad high-pass filter circuit and leapfrog filter circuit. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 95.24% in Case 1,87.09% in Case 2. This method exhibits higher diagnostic accuracy and reliability than existing methods.

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