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Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of Transfer Learning (TL) in knowledge transfer. As a result, DTL techniques can make DL-based fault diagnosis methods more reliable, robust and applicable, and they have been widely developed and investigated in the field of Intelligent Fault Diagnosis (IFD). Although several systematic and valuable review articles have been published on the topic of IFD, they summarized relevant research only from an algorithm perspective and overlooked practical applications in industry scenarios. Furthermore, a comprehensive review on DTL-based IFD methods is still lacking. From this insight, it is particularly important and more necessary to comprehensively survey the relevant publications of DTL-based IFD, which will …