Open AccessBearing Damage Detection using Support Vector Machine
Author(s) -
Sukendi,
Ikhwansyah Isranuri,
Suherman Suherman
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/851/1/012063
Subject(s) - damages , bearing (navigation) , vibration , support vector machine , computer science , work (physics) , condition monitoring , structural engineering , engineering , mechanical engineering , artificial intelligence , acoustics , electrical engineering , physics , political science , law
Vibration can be used for preventive condition monitoring in industrial equipment. Vibration measurement may avoid unexpected damages. The measurement data can be analyzed either by using signal processing or machine learning. This work assessed bearing damage detection as bearing is intensively used in industrial mechanical equipment. The assessment was performed experimentally by developing system prototype and measurement tools, including the use of laser vibrometer and a labjack as well as support vector machine to predict some types of damages. The work has been able to predict the BPFI, BPFO, BSF, and FTF damages by 93.125% accuracy. However, accuracy plunged to 61.25% when bearing damages were of three types: BPFO, BSF and FTF.