Electronic component fault diagnosis based on cross-domain features and deep contrastive learning

With the extensive use of electronic components in contemporary industry, fault diagnosis technology is more important in preserving equipment operation and improving output. Conventional fault diagnosis techniques limit their application in complicated fault situations by means of cross-domain feature extraction, which suffers limitations. This work thus suggests an electronic component fault diagnosis model called Cross-DeepContrastNet, which combines cross-domain feature extraction with deep contrastive learning and uses a series of training strategies to effectively extract discriminative features from many sources and types of data and acquire accurate fault diagnosis. Cross-DeepContrastNet beats conventional techniques in several respects, according to different experimental findings. Finally, further paths of investigation are suggested to solve the constraints of the use of the model in actual industries.