Integrationof Data Mining and Design of Experiment to Diagnosis Machine Efficiency

Abstract

The study focused on three phase induction motor because it is playing a vital role in
production development. The motor may get the problem in some parts may effect in the
wholeefficiency. Biannually maintenance increases the life of the machine, but many
companies, especially in the third nation, do the maintenance as soon as the machine gets
fail. The studyapplied two strategies to diagnosis and improve the efficiency of the machine.
Design of experiment conducted to extract the main effect and interactions between
variables. Three variables current, voltage, and power factor applied to understand the main
effect in the exploited power (P). Power factor recorded the most important factor impacts
the exploited power. Then, data mining utilized with two algorithms; random forests and
linear regression. These algorithms used to predict the power factor — six variables
collected for data mining, current, power losses, voltage, apparent power, resistance, and
exploited power. These variables used to predict the power factor. From both strategies, we
found that there is a strong relationship between exploited power and power factor, and
those variables have a positive impact on the efficiency of the machine. Also, power losses
and current have a negative impact on the power factor. Voltage did not give significant
important whether in the design of experiment or data mining. Therefore, power losses and
current must be controlled to keep the efficiency, and this can be done by regular
maintenance by professional workers.