Automation of predictive maintenance using internet of things (Iot) technology at university-based o&m project
| Keyword | Data-driven predictive maintenance planning framework; Internet of things (IoT) technology; Predictive maintenance; Six sigma DMAIC methodology |
| Authors | Fernandez J.R., Prasetyo Y.T., Persada S.F., Perwira Redi A.A.N. |
| satria.fadil@binus.ac.id | |
| Published Year | 2021 |
ABSTRACT
Predictive Maintenance can be defined as a type of advanced maintenance that detects the onset of system degradation allowing causal stressors to be eliminated or controlled prior to any significant deterioration in component physical state. Thru Internet of Things (IoT) Technology, automation, and implementation of Predictive Maintenance are possible. The purpose of this study is to propose the implementation of Predictive Maintenance using IpT Technology at University-based Operation & Maintenance Project that aims to transform the current Key Performance Indicator (KPI) of PM to CM Ratio from 80:20 to 90:10. Six Sigma DMAIC Methodology and Data-Driven Predictive Maintenance Planning Framework were utilized as the methodology of this research. Research’s results show that KPI, 90:10 (PM to CM Ratio) is achievable and maintenance cost can significantly reduce from 25% to 30%. Other valuable benefits are return of investment (10X), elimination of breakdown (70-75%), reduction in downtime (35%-45%) and increase of production (20%-25%). The proposed concept can be utilized in other industries to achieve high customer satisfaction percentages, sustainable operations, fault prediction, and online monitoring using PC or mobile applications. © 2021 by the authors.