In intelligent manufacturing, the prognostics and health management of the equipment driven by big data has been paid much attention. In recent years, because it can capture more hidden knowledge in the process of feature extraction of hierarchical structure and has good data adaptability across a variety of domains, deep learning has become a hot topic in the field of equipment health management. It has been widely used in equipment fault diagnostics and prognostics. This paper systematically reviews emerging literatures on the application of deep learning in equipment health management. It summarizes, classifies and explains main publications on this trendy topic. Various architectures and related theories are also discussed. As a review, this paper expounds the achievements, challenges and future development trends of the deep learning in the field of equipment fault diagnostics and prognostics. It provides a clear direction for practitioners including the industry to select, design or implement deep learning architecture for the equipment health management.