| dc.description.abstract | Machines are essential in both life and production. For life, a machine that performs
efficiently and smoothly contributes to the replacement of many labors, decreasing
workload and raising productivity. For production, machinery will help firms create
quicker, with higher product quality, and at a lower cost. Furthermore, the adoption
of technology provides firms with the required competitive advantages for long-term
success. A machine failure will affect a specific step of manufacturing, affecting the
entire process. This results in inefficient manufacturing lines, expensive repairs,
project delays, and decreased output. As a result, industrial operations demand
solutions that reduce maintenance costs while increasing equipment uptime and
availability. Predictive maintenance is a more efficient and cost-effective approach
for maintenance. Maintenance should be predicted using situation-appropriate
machine learning approaches. This thesis proposes the Artificial Neural Network
(ANN), Random Forest (RF), Support Vector Machine (SVM) and Extreme
Gradient Boosting (XGBoost) to predict the motor failure time. | en_US |
