Maintenance of the production machine plays an important role to keep the production process properly run. 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.

Original languageEnglish
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Issue numberAugust
Publication statusPublished - 2020
EventProceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, IOEM 2020 - Virtual, United States
Duration: 10 Aug 202014 Aug 2020


  • Cluster
  • Condition Monitoring
  • Data analytics
  • Predictive maintenance
  • Support Vector Regression


Dive into the research topics of 'Examining equipment condition monitoring for predictive maintenance, a case of typical process industry'. Together they form a unique fingerprint.

Cite this