Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis

Dong, Yutong; Jiang, Hongkai; Mu, Mingzhe; Wang, Xin

doi:10.1016/j.aei.2024.102573

Advanced Engineering Informatics

2024

DOI: 10.1016/j.aei.2024.102573

|View full text |Cite

Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis

Yutong Dong,

Hongkai Jiang,

Mingzhe Mu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 21 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

Tian,

Shen,

Wang

et al. 2024

Journal of Chemometrics

View full text Add to dashboard Cite

In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data.

show abstract

Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

Tian,

Shen,

Wang

et al. 2024

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

A task-oriented theil index-based meta-learning network with gradient calibration strategy for rotating machinery fault diagnosis with limited samples

Mu,

Jiang,

Wang

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

RTFNN: A refined time–frequency neural network for interpretable intelligent diagnosis of aero-engine

Ding,

Wang,

Qin

et al. 2025

Advanced Engineering Informatics

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis

Cited by 21 publications

References 34 publications

Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

A task-oriented theil index-based meta-learning network with gradient calibration strategy for rotating machinery fault diagnosis with limited samples

RTFNN: A refined time–frequency neural network for interpretable intelligent diagnosis of aero-engine

Contact Info

Product

Resources

About