Open AccessBearing degradation prediction based on support vector regression
Author(s) -
Sutawanir Darwis,
Nusar Hajarisman,
S. Suliadi,
Achmad Widodo
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/830/2/022006
Subject(s) - bearing (navigation) , degradation (telecommunications) , support vector machine , computer science , regression , regression analysis , data mining , reliability engineering , artificial intelligence , engineering , machine learning , statistics , mathematics , telecommunications
Predicting bearing degradation before reaching the state of risk of accident is one important issues in power generation insurance. This paper proposes a method based on support vector regression to achieve the goal. The method is applied on PRONOSTIA dataset which is an experimental platform dedicated to test methods related to bearing health assessment. The results show that the method can effectively model the evolution of the bearing degradation.