Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks

By Xiang Li, Wei Zhang, Qian Ding. IEEE transactions on industrial electronics (1982. Print), 2019

350 citations recorded by the source corpus.

Despite the recent advances on intelligent fault diagnosis of rolling element bearings, existing research works mostly assume training and testing data are drawn from the same distribution. However, due to variation of operating condition, domain shift phenomenon generally exists, which results in significant diagnosis performance deterioration. To address cross-domain problems, latest research works preferably apply domain adaptation techniques on marginal data distributions. However, it is usually assumed that sufficient testing data are available for training, that is not in accordance with most transfer tasks in real industries where only data in machine healthy condition can be collected in advance. This paper proposes a novel cross-domain fault diagnosis method based on deep generative neural networks. By artificially generating fake samples for domain adaptation, the proposed method is able to provide reliable cross-domain diagnosis results when testing data in machine fault conditions are not available for training. The experimental results suggest that the proposed method offers a promising approach for industrial applications.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/888f53905bbfe6ab98fe9be33d3afcf7e3801006

How researchers see this paper

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

In Control and Systems Engineering

1. Dhiraj Neupane
2. Paulo da Costa
3. Yihao Xue
4. Uzay Kaymak
5. Ming Zhao
6. Jing Lin
7. Mengjie Huang
8. Jong-Hoon Seok

In Mechanical Engineering

1. Dhiraj Neupane
2. Paulo da Costa
3. Uzay Kaymak
4. Ming Zhao
5. Jing Lin
6. Jong-Hoon Seok
7. Qin Wang
8. Shen Zhang

In Mechanics of Materials

1. Dhiraj Neupane
2. Yihao Xue
3. Ming Zhao
4. Jing Lin
5. Mengjie Huang
6. Jong-Hoon Seok
7. Rui Yang
8. Zidong Wang

Citations in the corpus

• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “In [163], a 2stage structure is proposed.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, positive): “In [141], the authors proposed a novel cross-domain fault diagnosis method based on deep GAN in which artificially generating the fake samples for domain adaptation, and their model provided the reliable cross-domain diagnosis results”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, methodology): “, 2016), and broader PHM applications (Babu et al., 2016; Jing et al., 2017; Li et al., 2018; Wang et al., 2019b).”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, positive): “To address the cross-domain diagnosis problem with insufficient target samples, Li et al. [216] used the MMD based generative convolutional networks to generate fake target fault samples and proposed a domain adaptation based approach for bearing fault diagnosis.”
• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, background): “Recently, Li et al. [50] proposed a deep generative model to generate fault target data using a labelled source domain and an unlabelled target domain.”
• 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].”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “Specially designed domain adaptation modules are combined with deep learning architectures to endow the domain transfer ability while maintaining the extraordinary automatic feature learning ability [156]–[160].”