Reliability, Availability, and Maintainability (RAM) are system design attributes that can have a substantial impact on the lifecycle cost and performance of an engineered system. The purpose of RAM studies is to ensure high production performance in maintaining high safety and quality level in any given industrial operation.
The objective of RAM Analysis
At its core, RAM studies entail representing a complex reality with a simplified model allowing for various types of analyses. Such a model can be used to predict performance, manage uncertainties and reduce conservatism. Results from the analyses should be used to give sound and unbiased decision support, as well as identify bottlenecks and main contributors to reduced performance and/or increased risk.
RAM studies provide input and decision support towards i.a.:
Predicted production performance and project economics;
Key production loss contributors;
Maintenance strategy and spare part philosophy;
Alternative technical or operational solutions (sensitivity studies);
Main uncertainties related to production performance;
Recommendations for improved production performance.
Methodology for RAM Studies:
At its core, RAM studies entail representing a complex reality with a simplified model allowing for various types of analyses. Such a model can be used to predict performance and manage uncertainties. Results from the analyses should be used to give sound and unbiased decision support and identify bottlenecks and main contributors to reduced performance and/or increased risk.
1. Establish a study basis
Identification of key assumptions and associated degrees of uncertainty is considered vital in order to effectively produce as realistic and accurate results as possible, and for giving input to sensitivity analyses that might be necessary to cater to uncertainties. The key assumptions will be documented in the RAM model study basis. Assumptions are typically categorized into technical, operational, and analytical assumptions.
Close cooperation with different disciplines in the project is necessary to ensure an understanding of the process and operation that results in a robust and realistic basis for the RAM model. Because of this, it is proposed to arrange for a work meeting or similar with relevant disciplines when establishing the RAM model study basis.
2. Required Input
A good understanding of the system to be analyzed is important for the RAM analysis to obtain as accurate results as possible. Typical client input for RAM analyses includes:
In addition to the above, one of the main premises for performing a successful RAM analysis is the use of appropriate reliability data. The application of data from literature and databases should always be thoroughly evaluated, to validate their relevance for the context in question. Reliability data sources include client experience data, OREDA, and the PDS handbook. ORS has access to a wide range of reliability data sources.
A Failure Mode, Effect, and Criticality Analysis (FMECA) if available is also a good input for the RAM analysis, especially for complex systems to give an accurate basis for system modeling.
3. Establish RAM model and run simulations
The RAM model study basis is used to establish the RAM model, typically represented by reliability block diagrams (RBDs). The Monte Carlo method is normally used for the RAM model simulations to produce uncertainty ranges and confidence levels for the estimates. ORS uses the software Miriam RAM Studio for this purpose.
4. Analyse the results
The results from the simulation are analyzed and reported depending on the objective of the RAM study in the best way to create value for the client, with some examples shown below.
Success rate probability distribution
ORS has supported a wide variety of clients within asset-intensive industries. ORS utilizes the advanced flow allocation software Miriam RAM Studio to produce fit-for-purpose RAM models. Contact Per Ståle Larsen to discuss how RAM Studies can benefit your company.