PROGNOSIS KERUSAKAN BANTALAN GELINDING DENGAN MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION (SVR)

Hevi Herlina, Ullu and Ir. Toni Prahasto, M.ASc, Ph.D and Dr. Achmad Widodo, ST, MT (2013) PROGNOSIS KERUSAKAN BANTALAN GELINDING DENGAN MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION (SVR). Masters thesis, Diponegoro University.

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Abstract

This research has been performed on the prognosis of rolling bearing damage using by Support Vector Regression (SVR). This research was performed using rolling bearings object as a rolling bearing component capable of making a machine continues to spin or work. Prognosis for rolling bearing damage can optimize maintenance costs because it can determine the remaining life of rolling bearings function before rolling bearing is damaged. Data derived from the extraction of some statistical features of the data trend rolling bearing vibration signal. The resulting feature data used in the process of learning and testing processes with SVR method, as it will result in rolling bearing damage prognosis approaching the ideal value of Root Mean Square Error (RMSE) and Coefisien Correlation (R). The results of this research is RMS feature is a good feature to perform the prognosis. RMSE and R values for RMS feature is close to the ideal value. RMSE values for RMS feature is 0.0129, while the value of R for RMS feature is 0.9709.

Item Type:Thesis (Masters)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:School of Postgraduate (mixed) > Master Program in Information System
ID Code:41213
Deposited By:Mr Musa MSI
Deposited On:06 Jan 2014 14:40
Last Modified:06 Jan 2014 14:40

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