A Model in Predictive Maintenance for a Manufacturing Company


  • Bethany Muyou Angkaus UTHM
  • Azizul Azhar Ramli Universiti Tun Hussein Onn Malaysia


Machine learning, linear regression, naive bayes, predictive maintenance


This document states the study of algorithms in predictive maintenance for a CMMS system, SynapseCore to effectively carry out maintenance activities and subsequently eases the burden of maintenance activities towards maintenance personnel. This research’s main objective is to apply linear regression and naïve bayes with parameters consisting of flowrate and different vibrations. The research uses the R programming language in RStudio to train the algorithm and follows the CRISP-DM process methodology. Results would show a graph comparing the predictions for both implemented algorithms including an evaluation of each algorithm’s performance. Through this research, the company would get a better insight on the implementation of different algorithms and how they affect the performance and prediction




How to Cite

Muyou Angkaus, B., & Ramli, A. A. (2022). A Model in Predictive Maintenance for a Manufacturing Company. Applied Information Technology And Computer Science, 3(2), 961–972. Retrieved from https://publisher.uthm.edu.my/periodicals/index.php/aitcs/article/view/7476