Data-Driven Soft Fault Diagnosis for Analog Circuits Based on Contrastive Learning
Zhou, Naixin Huang, Jiaxin Zhao, Yijiu Chen, Shibo Zhao, Jiaming Long, Feiyu
Zhou, Naixin
Huang, Jiaxin
Zhao, Yijiu
Chen, Shibo
Zhao, Jiaming
Long, Feiyu
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Analog circuits are fundamental to modern electrical systems, yet soft fault diagnosis of analog circuits remains challenging due to their nonlinear characteristics and the increasing scale of circuit integration. To address this issue, this article presents a novel data-driven fault diagnosis method utilizing contrastive learning (CL). In contrast to conventional data-driven approaches, the proposed method employs a self-supervised CL framework for representation learning, which captures informative features related to circuit faults. A temporal information-based encoder, leveraging 1-D convolutional neural networks (1D-CNNs) and a self-attention (SA) mechanism, is designed to enhance the extraction of latent patterns from raw time series data without relying on traditional signal processing techniques, thereby minimizing information loss and the need for expert knowledge. The encoder, trained using CL, is followed by a supervised two-layer neural network (NN) for fault classification. Simulation results on two benchmark filter circuits demonstrate the efficacy of the proposed method. Furthermore, the robustness of the encoder is validated under different faulty degrees, underscoring its reliability in various diagnostic scenarios.
Citation
N. Zhou, J. Huang, Y. Zhao, S. Chen, J. Zhao and F. Long, "Data-Driven Soft Fault Diagnosis for Analog Circuits Based on Contrastive Learning," in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-11, 2025, Art no. 3559311, doi: 10.1109/TIM.2025.3612648
Source
IEEE Transactions on Instrumentation and Measurement
Conference
Keywords
1-D convolutional neural networks (1D-CNNs), analog circuits, contrastive learning (CL), representation learning, self-attention (SA), soft fault diagnosis
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Publisher
IEEE