RANDOM FOREST-BASED PREDICTIVE MODEL FOR OPTIMIZING ASSET HEALTH MANAGEMENT IN POWER PLANTS
Effective asset management is crucial in the power plant industry to maintain operational reliability and minimize maintenance costs. The ability to accurately predict asset conditions is key to making informed decisions regarding maintenance and asset replacement. This research aims to develop a...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/86666 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Effective asset management is crucial in the power plant industry to maintain
operational reliability and minimize maintenance costs. The ability to accurately
predict asset conditions is key to making informed decisions regarding maintenance
and asset replacement. This research aims to develop a predictive model using the
Random Forest method for enhancing asset health management and maintenance
in power plants operated by a major power generation company in Indonesia. The
dataset used includes information such as Asset Criticality Ranking (ACR),
ACRRANK, System Criticality Ranking (SCR), Equipment Criticality Ranking
(ECR), and Maintenance Priority Index (MPI). The results demonstrate that the
Random Forest model achieves a predictive accuracy of 95% in predicting asset
conditions. Feature importance analysis reveals that MPI has the most significant
influence in predicting asset conditions, followed by ACR, ACRRANK and SCR.
This information can be utilized as a basis for decision-making regarding
maintenance and asset replacement, as well as identifying areas that require
improvement to maintain operational reliability in power plants.
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