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Kim, D., Park, S. and Lee, J. (2022) Self-Supervised Learning for Electronic Component Fault Diagnosis with Limited Labeled Data. Engineering Applications of Artificial Intelligence, 114, Article ID: 105196.

has been cited by the following article:

• TITLE: Application of Machine Learning in Electronic Device Fault Diagnosis

  AUTHORS: Mingqi Ma

  KEYWORDS: Machine Learning, Electronic Devices, Fault Diagnosis, Predictive Maintenance, Artificial Intelligence

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.11, November 26, 2024

  ABSTRACT: As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.