Introduction to Reliability, Availability, Maintainability Modeling

As global competition intensifies, industry is faced with a continuously increasing challenge to cut operating costs while improving quality and service, all the while being asked to meet the challenge at utmost speed with severely limited budgets.

One of the most effective means of increasing the productivity and profitability of any manufacturing or process plant is to improve reliability by minimizing the frequency of unplanned downtime and by reducing the length of time required for scheduled maintenance and changeovers. The basis for these goals starts with evaluating the engineering and design of the facility with regard to its Reliability, Availability, and Maintainability (RAM).

What Is Reliability, Availability, Maintainability Modeling?

A Reliability, Availability, Maintainability (RAM) study is a modeling technique used to simulate probable future performance and identify specific means for process design enhancement. The primary objective of the RAM study is to evaluate the current design and determine if there are alternate, more cost-effective system or equipment configurations which offer either improved reliability or provide for adequate reliability at a lower capital cost.

The technique involves a systems engineering approach, whereby a pre-defined system is broken down into its individual assets to determine how each of these, and combinations thereof, affect the whole. In this manner, the RAM analyst can determine all causes of potential outages and evaluate the criticality of each. The output is used to quantify the economics or other performance criteria of equipment-related decisions such as redundancy, spare parts, equipment sizing, maintenance practices and policies, quality of components, etc.

Challenges of RAM Modeling

Undoubtedly the advent of RAM models was a large step in evaluating the engineering and design of a facility. However, it is not without its limitations. For instance:

  • RAM Modeling tools utilize Monte Carlo (MC) simulations that rely on static data, static variables, and typically many assumptions. Although the MC gains accuracy by introducing an element of randomness into the calculations, they can paint an unrealistic picture of returns, or are found to be inaccurate when compared to the actual results once the facility is in a steady state of operation.
  • RAM is great for complex facility simulations, but because it relies on set inputs and variables and not actual data from the equipment or process, it is merely an estimation of future performance based on a snapshot in time that is not evergreened.
  • RAM Models can take a long time to run (sometimes days), depending on complexity.
  • Models are typically completed during the front-end engineering design (FEED) stage of engineering.
  • The simulation modeler must accurately identify cause-and-effect relationships. A common mistake made by many organizations in developing a simulation program is to assign the model building responsibility to someone proficient in computer technology but lacking in practical knowledge of the system being modeled.

But what if there was a way to combine the quantitative approach of asset specific analysis with system complexity simulation of RAM?

Data-Driven Reliability: The Next Generation of Simulation Modeling

RAM Modeling has helped industry become not only reliable but more cost effective. However, industry is continuing to see major advancements in data acquisition, modeling, and analytics. With these capabilities in mind, we now have the opportunity to take the next leap in reliability analysis, allowing us to improve probability modeling while optimizing total maintenance and inspection spend.

This leap is being made possible through Quantitative Reliability Optimization (QRO). QRO is an approach to reliability modeling which connects every relevant reliability data point at a complex facility to one integrated model, allowing for near real time complex decision making and simulated analysis.

Below are some examples of how QRO will elevate your reliability modeling:

  • QRO utilizes a variety of statistical and numerical models to perform quickly, greatly reducing the run time of the model.
  • Data-Driven Reliability Modeling uses actual live data to run the simulations. This allows you to introduce and update new data, rerun the model, and see what impacts are being made to the facility based on the actual condition data of the assets and process data of the systems.
  • QRO not only focuses on the Reliability and Availability but puts equal emphasis on maintainability by letting you understand the impact of every inspection or maintenance activity performed.
  • QRO helps you understand the impact of every piece of data that is currently being gathered or could possibly be gathered in the future.
  • QRO provides near real-time modeling, including the implications of moving a turnaround, feedstock changes, or various capital projects.
  • QRO will help drive effective decisions in the event of integrity or reliability based operating excursions.

Learn more about Quantitative Reliability Optimization (QRO).

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