Abstract

Maintenance of the production machine plays an important role to keep the production process running. Improper maintenance may cause problems in production. Corrective Maintenance strategy, a response to equipment failure, is needed to rectify the failures. However, this strategy may cause higher downtime and higher costs due to loss of production. Predictive Maintenance (refer to Condition-Based Maintenance) utilizing equipment monitoring data can be used for optimizing the maintenance strategy by predicting the future machine condition. This research attempts to examine the condition-based equipment data using the data analytics approach to developing a Predictive Maintenance program. Several methods are applied. K-means for clustering the failure characteristic, Support Vector Regression (SVR) model used for predicting equipment failure. The result and discussion represent that SVR and K-means model suitable for developing equipment failure prediction, useful support for managing the maintenance activities