A Review of Electromagnetic Compatibility Fault Diagnosis Based on Machine Learning
With the rapid development of electronic technology,the demand for improving the performance of electron-ic devices is constantly increasing,making it crucial to ensure the stability of components in the devices.Among them,the evaluation of electromagnetic compatibility is significant for accurately judging the status of components.However,traditional electromagnetic compatibility fault diagnosis methods have many limitations and are difficult to meet the demands of modern electronic devices.Driven by artificial intelligence technology,electromagnetic compatibility fault diagnosis methods based on machine learning have received widespread attention.This article focuses on electro-magnetic compatibility fault classification,conducting a thorough study and comparative analysis of traditional diag-nostic methods currently applied in this field,as well as three types of machine learning fault diagnosis methods,sup-port vector machine(SVM),BP neural network based on traditional machine learning,and convolutional neural net-work(CNN)based on deep learning.The article discusses the advantages and disadvantages of these methods.Finally,the development of machine learning in the field of electromagnetic compatibility fault diagnosis is summarized and prospected,believing that this field has broad application prospects and the value of further research.
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