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A research agenda of AI-based analog circuit fault diagnosis with bibliometric analysis
- 1 The University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL
* Author to whom correspondence should be addressed.
Abstract
Analog circuit fault identification is crucial for preserving regular operation. Methods based on artificial intelligence (AI) provide excellent accuracy in fault identification. AI-based techniques have been used widely in recent years, displaying remarkable variety and complexity. Therefore, in order to have a clearer understanding of the issues in this area, it is necessary to summarise and classify the techniques. In this article, various AI-based fault detection techniques are displayed, and bibliometrics is used to show the trend and citations. The relationships, significant writers, and journals will first be discussed in this article, after which some key pieces of literature will be displayed. The key findings and conclusions, clarification of the current issues, and a summary of the present and foreseeable research trends are all included in the last section.
Keywords
analogy circuit, fault diagnosis, deep learning, machine learning, bibliometric analysis.
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Cite this article
Zhou,J. (2023). A research agenda of AI-based analog circuit fault diagnosis with bibliometric analysis. Applied and Computational Engineering,6,325-333.
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