Dynamic Criticality-Based Maintenance
There’s an increasing need in the industry to prioritize maintenance activities and investments based on the criticality and associated risk of assets. A review of industry and literature show that:
- Current criticality analysis techniques consider criticality as more or less a static quantity that is not updated with sufficient frequency as the operating environment changes.
- There’s need to continuously monitor, review and update the criticality of assets to ensure maintenance objectives for the assets are aligned to business needs.
- Identify factors that affect, and influences changes to, an asset’s criticality.
- Develop a technique to monitor, identify and detect changes in criticality.
- Develop a business process for updating asset criticality in a company.
- Use real-time criticality for making optimum maintenance decisions.
Data collection: Integrated information system
- Dynamic linkage of remote data sources using data fusion as an integration model
- Data retrieval architecture using standardized request for text and numbers
Model Buildings: multi-stage model for
Criticality factor data; dynamic analysis of criticality; linking maintenance decision & performance to asset criticality. MCDM for factor weightings.
Dynamically generate optimal maintenance plans for the system considering individual asset criticality and system service level requirements.
- A novel rational working procedure/model for criticality-based maintenance.
- A quantitative tool for updating criticality and providing information for optimal maintenance decisions for the assets in the company.
- Real-time criticality could ensure limited maintenance resources are spent on the most criticality assets.
- Maintenance decisions can be made with current, not historical, data. Decision makers have better visibility over the system.
- Maintenance objectives are properly aligned to business objectives.
Acknowledgement to the Petroleum Technology Development Fund.