Machine Learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy

Mudabbir, Mohammed; Mosavi, Amirhosein

doi:10.1109/ines59282.2023.10297884

2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES) 2023

DOI: 10.1109/ines59282.2023.10297884

|View full text |Cite

Machine Learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy

Mohammed Mudabbir,

Amirhosein Mosavi

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...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

Vietze,

Pellkofer,

Stahl

2024

Machines

View full text Add to dashboard Cite

Calculating the service life of gears under variable loads requires a description of the load-carrying capacity. The current standard for this is the use of the S/N curve. International standards such as ISO 6336 stipulate the use of this approach for the calculation of the service of gears under variable loads. In this paper, five new approaches are developed and evaluated to describe the load-carrying capacity of gears in the load range of finite life. Four methods are based on machine learning, and one uses mathematical regression. To validate the new approaches, the results of an experimental study investigating the service life of gears under variable loads are presented. These results form the basis for the conducted study, which compares the five new methods with the existing approach. The comparison focuses on the ability of the load-carrying capacity descriptions to provide an accurate calculation of the service life and to reduce scattering as much as possible. The results of the study show significant potential for the new methods, especially the one based on a neural network.

show abstract

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

Vietze,

Pellkofer,

Stahl

2024

Machines

View full text Add to dashboard Cite

show abstract

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.

Machine Learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy

Cited by 1 publication

References 43 publications

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

Contact Info

Product

Resources

About