A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
نویسندگان
چکیده
Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages (DL) in feature representation, but also benefit from superiority (TL) knowledge transfer. As result, DTL techniques make DL-based fault diagnosis methods more reliable, robust and applicable, they have been widely developed investigated field Intelligent Fault Diagnosis (IFD). Although several systematic valuable review articles published on topic IFD, summarized relevant research an algorithm perspective overlooked practical applications industry scenarios. Furthermore, comprehensive DTL-based IFD still lacking. From this insight, it particularly important necessary to comprehensively survey publications will help readers conveniently understand current state-of-the-art quickly design effective solution for solving problems practice. First, theoretical backgrounds are briefly introduced present how transfer learning be integrated with deep models. Then, major their recent developments detailed discussed. More importantly, suggestions select algorithms applications, some future challenges shared. Finally, conclusions given. At last, we reason believe that works done article provide convenience inspiration researchers who want devote efforts progress advance IFD.
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ژورنال
عنوان ژورنال: Mechanical Systems and Signal Processing
سال: 2022
ISSN: ['1096-1216', '0888-3270']
DOI: https://doi.org/10.1016/j.ymssp.2021.108487