2022
DOI: 10.26855/ea.2022.06.001
|View full text |Cite
|
Sign up to set email alerts
|

Gearbox Multiple Faults Diagnosis under Stationary and Non-Stationary Operating Conditions Using Convolutional Neural Networks

Abstract: High accuracy in gearbox fault diagnosis is of paramount importance for keeping industrial systems safe and working normally. Concerning various single or multiple faults diagnosis using classical machine learning algorithms, the feature extraction and selection methods are time-consuming and labor-intensive processes requiring expert knowledge of the relevant features related to the system. To mitigate this problem, a deep learning convolutional neural network (CNN) is proposed which enables automatic feature… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 14 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?