Record Detail
Advanced Search
Text
Transfer Across Different Machines by Transfer Function Estimation
A digital twin is a promising evolving tool for prognostic health monitoring. However, in rotating machinery, the transfer function between the rotating components and the sensor distorts the vibration signal, hence, complicating the ability to apply a digital twin to new systems. This paper demonstrates the importance of estimating the transfer function for a successful transfer across different machines (TDM). Furthermore, there are few algorithms in the literature for transfer function estimation. The current algorithms can estimate the magnitude of the transfer function without its original phase. In this study, a new approach is presented that enables the estimation of the transfer function with its phase for a gear signal. The performance of the new algorithm is demonstrated by measured signals and by a simulated transfer function.
Availability
No copy data
Detail Information
Series Title |
-
|
---|---|
Call Number |
-
|
Publisher | Frontiers in Artificial Intelligence : Switzerland., 2022 |
Collation |
006
|
Language |
English
|
ISBN/ISSN |
2624-8212
|
Classification |
NONE
|
Content Type |
-
|
Media Type |
-
|
---|---|
Carrier Type |
-
|
Edition |
-
|
Subject(s) | |
Specific Detail Info |
-
|
Statement of Responsibility |
-
|
Other Information
Accreditation |
Scopus Q3
|
---|
Other version/related
No other version available
File Attachment
Information
Web Online Public Access Catalog - Use the search options to find documents quickly