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

Abstract

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 \url{https://github.com/ZhaoZhibin/UDTL}.

This paper has been accepted by IEEE Transactions on Instrumentation and Measurement

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• How transferable are features in deep neural networks?
• Learning Transferable Features with Deep Adaptation Networks
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Deep Domain Confusion: Maximizing for Domain Invariance
• Conditional Adversarial Domain Adaptation
• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study
• Secure Federated Transfer Learning
• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application
• Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning
• Revisiting Batch Normalization For Practical Domain Adaptation
• Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
• Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
• Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition

Cited by in corpus (3)

• A Survey on Negative Transfer
• Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
• Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis