Aerial view terminal

Heath Stephens Discusses Reliability, Availability and Maintainability (RAM) Modeling in Industrial Design

08.18.2022

Heath Stephens, digitalization leader with Hargrove Controls & Automation, a certified member of the Control System Integrators Association (CSIA), authored a Smart Industry Forum article discussing how reliability, availability, and maintainability modeling can transform industrial design.

 Read the original article titled, “How reliability, availability, and maintainability modeling reshapes industrial design” on the Smart Industry website.

The insights of reliability, availability, and maintainability (RAM) modeling are becoming increasingly significant to unlocking the full potential of plant equipment and projects according to Heath Stephens, digitalization leader with Hargrove Controls & Automation. Controls engineers must frequently overcome design deficiency, but not all strategies result in the most advantageous solution. A project is often considered complete if the outcome was programmed per the narrative and all the systems are successfully operating within the given parameters.  

But has the performance really been optimized? Heath Stephens says, “To achieve next-level plant optimization, we must expand our parameters and question assumptions. This is where RAM modeling comes into the picture.”

RAM modeling looks at a system’s capabilities, identifies potential causes of losses, and establishes an expected uptime of an entire production facility. Ordinarily used in the design phase, it calculates the predicted OEE (overall equipment effectiveness) and proves individual contributions to OEE. However, this modeling can be used for so much more. “RAM modeling performs Monte Carlo simulations of the plant life cycle, basically hundreds of ‘rolls of the dice’ to determine the odds of various combinations of failure and availability patterns that predict the uptime of your plant. It’s a level of design verification beyond the classic mass and heat-flow calculations or reaction-kinetics predictions,” says Stephens. Once you obtain this information you can make stronger decisions, assess inventory needs, evaluate raw-material supply plans, and improve entire operational strategies.

For a controls engineer, RAM modeling can help determine what should be programmed without wasting time on a suboptimal strategy.

Examples given by Stephens ask the following questions:

  • Should a two-pump bank be operated in parallel or alternate every transfer, after X hours of runtime, by operator command, or only upon failure?
  • If a buffer tank is nearing capacity, is it better to run at full rates and stop at the high-level limit or reduce rates to buy more time?
  • For a multi-product plant, what is the most cost-effective way to operate if partial equipment failures prevent the production of certain products?

Despite the benefits, RAM model study is considered outside of the scope of most automation engineers’ responsibilities. It is a system-level team effort that requires a variety of viewpoints from stake holders with different skills and backgrounds. Contributors should include process design, production, maintenance, process control engineers, and more. “The study can be led by any one of those members or even an outside third party,” suggests Stephens.

“As I’ve progressed in my process control engineering career, I have gone from simply programming what I was told, to questioning my instructions, to providing input into operating procedures and even process design decisions. My knowledge and experience have now helped create more reliable and efficient plants. Armed with RAM data, the process control engineer can do more than just optimize loop tuning but optimize the process control strategy and take plant automation to the next level.”

Read the original article titled, “How reliability, availability, and maintainability modeling reshapes industrial design” on the Smart Industry website.

Back to News
Share Next Article Blue arrow 08.18.2022
Hargrove’s One Team Workplace