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Digital equipment has the characteristics of complex structure,intensive technology ,and high level of information.Traditional fault diagnosis methods require multiple components to be disassembled and have low accuracy in fault localization.But deep learning can extract valuable and sensitive features from equipment raw data,making it suitable for intelligent fault diagnosis of digital equipment.For this purpose,the practical difficulties and challenges of digital equipment fault diagnosis in the military were first analyzed,and the research status of digital equipment maintenance support at home and abroad was elaborated;then,the main methods and research progress of equipment fault diagnosis were summarized,with a focus on sorting out the research results of deep learning in the field of equipment fault diagnosis;finaiiy,three research ideas for implementing digital equipment fault diagnosis based on deep learning methods were proposed in combination with practical applications.