DIAGNOSIS KERUSAKAN BANTALAN GELINDING MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN)

Mariza, Devega (2013) DIAGNOSIS KERUSAKAN BANTALAN GELINDING MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN). PhD thesis, Diponegoro University.

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Official URL: http://www.msi.undip.ac.id

Abstract

Fault diagnosis of rolling element bearings on industrial machinery has been investegated in this research. This research was condected because a rolling element bearings is one of the vital parts on the rotating machine that hold an important role. Faulty bearings make the fatal effect and company loss Therefore a fault diagnosi is important to prevent the disadvantage the damage to other components of a machines. Fault diagnosis conducted with classifying eight types of fault. This eight types of fault are kind of fault that commonly occured in rolling element bearings. This research starts from feature extraction, feature selection, dan classification process. The classification process is using Radial Basis Function Neural Network (RBFNN). Results showed that the RBFNN has quite good performance in classifying. This is can be seen on the accuracy from each class.

Item Type:Thesis (PhD)
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Engineering > Department of Mechanical Engineering
Faculty of Engineering > Department of Mechanical Engineering
ID Code:39525
Deposited By:Mr Musa MSI
Deposited On:15 Jul 2013 09:41
Last Modified:14 Mar 2016 21:37

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