Deep Model Based Domain Adaptation for Fault Diagnosis

By Weining Lu, Bin Liang, Yu Cheng, Deshan Meng, Jun Yang, Zhang Tao. IEEE transactions on industrial electronics (1982. Print), 2017

679 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/bc2bc5669141202972bc512d646149315e2cdd1d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Long Wen
2. Liang Gao
3. Xinyu Li
4. Chunhui Zhao
5. Jianqiao Sun
6. Paulo da Costa
7. Qian Ding
8. Dongxiang Jiang

In Control and Systems Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler
5. Xiang Li
6. Liang Gao
7. Xinyu Li
8. Long Wen

Citations in the corpus

• Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis (2021, methodology): “The sparse autoencoder was also utilized by Lu et al. [16] for the deep domain adaptation of training and target faults.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “i) Statistic-based methods: As mentioned in previous paragraphs, MMD and its variants are widely used in statistic-based transfer learning methods [50], [69], [111], [126], [131], [132].”
• Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, methodology): “Two handcrafted-feature-based methods, TCA and DAFD, are applied to compare with the DCTLN.”
• Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks (2020, background): “A number of deep transfer learning approaches have been proposed to improve the scalability and applicability of fault diagnosis methods [23]–[26].”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, methodology): “[30] applied a novel deep neural network model with domain adaptation”
• A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning (2019, neutral): “[34] introduced a deep neural network with domain adaption for fault diagnosis, and they also proposed exploring strategies for optimal hyper parameters.”
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, methodology): “Another metric distance, called MMD, was widely used in the field of intelligent diagnosis [77]–[85].”
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, background): “In [38], the authors proposed a deep transfer network based domain adaptation method for bearing and gearbox diagnosis across diverse operating conditions.”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “ability while maintaining the extraordinary automatic feature learning ability [111], [158]–[161].”
• Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019, methodology): “However, the network weights have high probability of dropping to near-zeros without proper regularizations using this method [60].”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “By exploring domain-invariant features, domain adaptation establishes the knowledge transfer from the source domain to the target domain [156].”
• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, methodology): “In PHM, Lu et al. [46] proposed a method for domain adaptation where Maximum-Mean Discrepancy (MMD) discrepancy metric is used to find a common latent space in a clas-sification task.”
• Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks (2019, methodology): “[18], where the concerned transfer task is similar with T03 in this paper.”