| Abstrak/Abstract |
Bearing is a mechanical element of machine to
reduce friction between two rotating objects. Bearings have
an optimal lifetime but in real application, not all of them
are able to reach their lifetime and most of them are
damaged in a relatively short time. Any flaw on bearing
that is not addressed earlier can lead to operation failure on
mechanical equipment or machines. Usually type of bearing
damage can be used to identify the real cause of the failure.
In order to do a correct measure, accurate information
about the type of bearing damage is needed. In this
research, the bearing fault analysis is done using modified
ANFIS method. Training data and test data used in this
research were taken from Case Western Reserved
University. Training data was observed in time and
frequency domains. Observation results in time domain are
root mean square (RMS) and kurtosis values. In frequency
domain, data was filtered using Hilbert transformation to
obtain the maximum value of amplitude and frequency.
The RMS, kurtosis, amplitude, and frequency were used as
input parameters on modified ANFIS. The results has
shown that the system can identify bearing damage in
accordance with the type and level of the damage with
success rate is 99.61 percent. |
