Getting The Best Out of RAM Studies Throughout The Lifecycle
Designers and operators are carrying out reliability studies essentially to optimize project profitability as a function of income and expense factors such as capital expenditures, operating expenditures, production rate, the frequency of equipment failures and the associated downtime for such failures.
Reliability Availability and Maintainability (RAM) studies are used to predict and identify ways to improve system availability, throughput performance and the efficiency of a process plant from a cost/benefit point of view during the lifecycle of the plant. The model generated require data in real time to predict capacity, as in uptime or production forecasts. The integration of accurate failure event documentation into the company maintenance system and importing near real time hardware data is a significant amalgamation that shall make asset integrity management a more functional engineering driven exercise.
There is an aspect of big data analytics which involves collecting data such as maintenance records, operational logs, and historical data. These shall provide valuable information on system behaviour, failure events, maintenance activities, and environmental conditions. The collected data can then be integrated into a centralized repository for big data analytics. Knowing this, the system design can be optimized, including its configuration, level of redundancy, component selection and support optimization and improvement in maintenance and operational strategies throughout the asset or plant lifecycle. While suggesting tangible improvements, a RAM program can provide confidence that the system will meet its operational targets and support wider project decision-making.