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The structure and function of the integrated equipment is increasingly complex, traditional fault diagnosis methods are difficult to extract accurate features. Deep learning has strong learning ability, which can effectively mine features and is suitable for fault diagnosis of integrated equipment. For this purpose, firstly, the latest research progress of fault diagnosis based on deep learning at home and abroad is described according to the different application fields; secondly, three typical fault diagnosis methods based on deep learning (deep belief networks, stacked auto-encoders and convolutional neural networks) are briefly described, the achievements and challenges are sorted out and summarizes are made; then, the challenges of fault diagnosis based on deep learning are summarized into six types, and the research ideas are proposed according to the research results summarized above; finally, combined with the actual application, four development directions are proposed.