Vibration Analysis in Turbomachines Using Machine Learning Techniques

Allan Alves Pinheiro, Iago Modesto Brandao and Cesar Da Costa
Additional contact information
Allan Alves Pinheiro: IFSP- Federal Institute of Sao Paulo
Iago Modesto Brandao: IFSP- Federal Institute of Sao Paulo
Cesar Da Costa: IFSP - Federal Institute of Sao Paulo

European Journal of Engineering and Technology Research, 2019, vol. 4, issue 2, 12-16

Abstract: This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.

Keywords: Machine Learning; Fault Diagnosis; Vibration Analysis; Fault Classification (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/61128 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/61128/12036 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:4:y:2019:i:2:id:61128

DOI: 10.24018/ejeng.2019.4.2.1128

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().