Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach

Blade fault localization with the use of vibration signals through artificial neural network: a data-driven approach

Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques f...

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Bibliographic Details
Main Authors: Keng, Ngui Wai, Mohd Salman, Leong, Mohd Ibrahim, Shapiai, Hee, Lim Meng
Format: Article
Language:English
Published: UPM Press 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Online Access:http://umpir.ump.edu.my/id/eprint/38233/
http://umpir.ump.edu.my/id/eprint/38233/1/Blade%20fault%20localization%20with%20the%20use%20of%20vibration%20signals%20through%20artificial%20neural%20network_.pdf

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http://umpir.ump.edu.my/id/eprint/38233/
http://umpir.ump.edu.my/id/eprint/38233/1/Blade%20fault%20localization%20with%20the%20use%20of%20vibration%20signals%20through%20artificial%20neural%20network_.pdf

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