mlsanityData to 14 Oct 2026

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

arXiv 1912.12528 ↗First posted 28 Dec 2019eess.SP cs.LGcited by 19

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Abstract (arXiv)

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem. Although it has achieved huge development, a standard and open source code framework as well as a comparative study for UDTL-based IFD are not yet established. In this paper, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD which are rarely studied, including transferability of features, influence of backbones, negative transfer, physical priors, etc. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at urlhttps://github.com/ZhaoZhibin/UDTL.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Dec 2019.

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Citing papers per year: 2021: 1; 2022: 0; 2023: 8; 2024: 2; 2025: 5; 2026: 3.

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  1. 2023HUST bearing: a practical dataset for ball bearing fault diagnosiscited by 9
  2. 2023ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmapscited by 8
  3. 2021Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosiscited by 6
  4. 2023Large Scale Foundation Models for Intelligent Manufacturing Applications: A Surveycited by 6
  5. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  6. 2025UniFault: A Fault Diagnosis Foundation Model from Bearing Datacited by 3
  7. 2023A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cyclescited by 1
  8. 2023Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarioscited by 0
  9. 2023Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosiscited by 0
  10. 2023Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosiscited by 0
  11. 2023FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learningcited by 0
  12. 2024Transfer learning of state-based potential games for process optimization in decentralized manufacturing systemscited by 0
7 more
  1. 2025Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosiscited by 0
  2. 2025PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraintcited by 0
  3. 2025Industrial Steel Slag Flow Data Loading Method for Deep Learning Applicationscited by 0
  4. 2025ATLO-ML: Adaptive Time-Length Optimizer for Machine Learning -- Insights from Air Quality Forecastingcited by 0
  5. 2026Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognosticscited by 0
  6. 2026When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Modelscited by 0
  7. 2026GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networkscited by 0