Small sample gearbox fault diagnosis based on improved deep forest in noisy environments

Shao, Haidong; Ming, Yuhang; Liu, Yiyu; Liu, Bin

doi:10.1080/10589759.2024.2404489

Nondestructive Testing and Evaluation

2024

DOI: 10.1080/10589759.2024.2404489

|View full text |Cite

Small sample gearbox fault diagnosis based on improved deep forest in noisy environments

Haidong Shao,

Yuhang Ming,

Yiyu Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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.

Small sample gearbox fault diagnosis based on improved deep forest in noisy environments

Cited by 3 publications

References 34 publications

ARLSSMM: an effective fault diagnosis method for roller bearing under strong noise

ARLSSMM: an effective fault diagnosis method for roller bearing under strong noise

A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis

Data enhanced deep transfer learning for health state evaluation of cutting tools

Contact Info

Product

Resources

About