TY  - JOUR
ID  - eprints20278
UR  - http://digilib.unila.ac.id/20278/
A1  - NN, Bagus Sulistyo
Y1  - 2012/01/25/
N2  - In order to keep an ongoing electricity generating business, it is crucial to
optimize the maintenance of the power equipment and auxiliary, especially in
predictive maintenance. With a proper predictive maintenance, early failure of the
equipment could be detected so that, a catastrophic damage could be prevented.
In this paper, the predictive maintenance of conditional monitoring base rotor
generator with neural network som-kohonen (som-kohonen). Is examining are use
as the main information of conditional monitoring base rotor generator as input
parameter of som-kohonen. Som-Kohonen is an unsupervised learning so that no
prior knowledge of a trainer is needed. Som-Kohonen could simplify the complex
and high dimensional data input into low dimensional data output in an iterative
and adaptive learning way, and the right information would appear from this
extraction.
The goal of this paper is to acquire the index of failure of condition monitoring
base rotor generator, and to make it a prior knowledge and standard of the next
predictive maintenance of condition monitoring base rotor generator.
JF  - Digital Library
TI  - CONDITIONAL MONITORING BASE ROTOR GENERATOR BASED
VIBRATION WITH NEURAL-NETWORK SOM-KOHONEN
APPROACHING IN GENERATOR 125.5 MVA iSTUDY CASE.
AV  - public
ER  -