Affiliation:
1. College of Marine Engineering, Dalian Maritime University, Dalian 116026, China
Abstract
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge transference. This synergistic integration propels DTL to the forefront of research and development within the Intelligent Fault Diagnosis (IFD) sphere. While the early DTL paradigms, reliant on fine-tuning, demonstrated effectiveness, they encountered considerable obstacles in complex domains. In response to these challenges, Adversarial Deep Transfer Learning (ADTL) emerged. This review first categorizes ADTL into non-generative and generative models. The former expands upon traditional DTL, focusing on the efficient transference of features and mapping relationships, while the latter employs technologies such as Generative Adversarial Networks (GANs) to facilitate feature transformation. A thorough examination of the recent advancements of ADTL in the IFD field follows. The review concludes by summarizing the current challenges and future directions for DTL in fault diagnosis, including issues such as data imbalance, negative transfer, and adversarial training stability. Through this cohesive analysis, this review aims to offer valuable insights and guidance for the optimization and implementation of ADTL in real-world industrial scenarios.
Funder
National Major Scientific Research Instrument Development Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference234 articles.
1. Sun, S., Shen, C., and Wang, D. (2023). Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques. Sensors, 23.
2. Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study;Zhao;IEEE Trans. Instrum. Meas.,2021
3. A Systematic Review of Deep Transfer Learning for Machinery Fault Diagnosis;Li;Neurocomputing,2020
4. Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge;Qian;Neural Process Lett.,2022
5. A Perspective Survey on Deep Transfer Learning for Fault Diagnosis in Industrial Scenarios: Theories, Applications and Challenges;Li;Mech. Syst. Signal Process.,2022