RAM analysis for new technologies - What you need to know
Updated: Jun 12, 2023
One of the main premises for performing a successful Reliability, Availability, and Maintainability (RAM) analysis is the use of appropriate assumptions and reliability data. The unconscious application of data from literature and databases is seldom the best approach for RAM studies. This pitfall is particularly important to avoid when considering new technology or new applications of known technology.
Examples of such cases can be:
Production performance effects from the installation of a carbon capture system on an existing plant.
Application of hydrogen as the energy source for critical services and associated stringent availability requirements.
Production performance effect when changing equipment driver and process systems from fuel to electrical.
In cases like the ones above, reliability and availability of data and assumptions are key inputs for predicting production performance. However, the availability of empirical and representative data is scarce. Application of reliable data from renowned sources from other industries “just because the equipment type resembles, and data is available” will provide uncertain and in the worst case erroneous results and poor decision support.
To overcome these uncertainties and establish better decision support, it is advisable to consider applying a risk-based approach and tailoring the reliability of data.
Focusing on the Uncertainties when Performing a RAM Analysis
A risk-based approach could ensure resilience against the inherent uncertainty associated with new technology or new applications of known technologies. Shifting of focus from failure rates and MTBF discussions to consequences and uncertainty will better facilitate a process that identifies measures to increase resilience and robustness with regard to production performance.
Focusing on the Uncertainties when Performing a RAM Analysis
Tailoring of Reliability Data is when data from other activities are adapted to other purposes based on expert knowledge. You can use the application of e.g. Bayesian statistical methods for this process. The method combines prior knowledge (prior distribution) with expert knowledge (likelihood function) resulting in posterior knowledge (posterior distribution). The posterior knowledge results in data that are more realistic to the scenario in question.
Would you like to know more about the purpose of RAM analyses or how to perform a RAM analysis? Feel free to get in touch with us.
