RxM: What is prescriptive maintenance, and how soon will you need it?

Early adopters are exploring how outcome-focused approaches to maintenance can enhance asset management.

By Sheila Kennedy, contributing editor

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Don’t just predict problems – prescribe a solution. That’s the premise behind prescriptive maintenance, which as a concept goes hand-in-hand with prescriptive analytics. Odds are you’ll be hearing these new buzzwords a lot more often in the coming months and years. But what is prescriptive maintenance, really? How does it work? And maybe of most importance, what can it achieve that other models can’t?

First, to better differentiate the words prescriptive and predictive, the word “prescriptive” here will be used interchangeably with “Rx.”

Rx maintenance is unique in that instead of just predicting impending failure, as predictive maintenance (PdM) does, it strives to produce outcome-focused recommendations for operations and maintenance from the Rx analytics. Though RxM is still in its infancy, many thought leaders are considering its potential to become the next level of reliability and maintenance best practice.

Analysts define Rx strategies

One of the earlier voices on prescriptive maintenance was Dan Miklovic, principal analyst at LNS Research. “No longer will you need an ensemble of experts to tell you how and when to maintain your assets, as the assets themselves will tell you what they need if they are unable to fix themselves,” wrote Miklovic in a May 2016 blog post, “What Comes After Predictive Maintenance?”

He suggested the acronym RxM at that time, and he continues to research the topic (see Figure 1). Better and more data, coupled with Big Data tools that can interpret things such as the content of repair manuals, is the key to unlocking the concept of RxM, Miklovic says today.

It starts with prescriptive analytics, which not only tells you that a problem is likely to emerge, but also it gives you multiple response scenarios from which to choose. “Let’s say a piece of equipment is showing increasing bearing temperature,” Miklovic explains. “Predictive analytics looks at the temperature profile and tells you it is likely to fail in X amount of time. On the other hand, prescriptive analytics tells you that if you slow the equipment down by Y%, the time to failure can be doubled, putting you within the already scheduled maintenance window and revealing whether you can still meet planned production requirements.”

Another early follower of this trend is Ralph Rio, vice president of enterprise software at ARC Advisory Group. “From my experience with clients from both the user and supplier side, the dominant application right now is PdM – prescriptive maintenance is beyond that; it’s new thought leadership,” he says. “But the goals of PdM and prescriptive maintenance are similar: to reduce unplanned downtime, which causes lost revenues, materials, and labor.”

To help clients better differentiate the newer approaches from conventional maintenance strategies, Rio developed the Asset Performance Management Maturity Model (see Figure 2). The upper tiers of maintenance maturity – predictive and prescriptive maintenance – are both multivariate approaches. The current in a pump’s motor drive, the fluid going into the pump, its temperature, and the pressure going in and out can all be combined to better assess the health of the pump and motor, so you get longer advance warning of a failure and can make changes during a planned shutdown, he explains. The industrial internet of things (IIoT) provides the data, and analytics generate the alerts.

Prescriptive maintenance adds the ability to give advice to the technician on what to do and how to do the repair by taking advantage of artificial intelligence (AI) and machine learning. The math algorithms are more detailed, and there’s some intelligence added to give the technician some direction.

The three lower tiers of Rio’s model include single-variable condition-based maintenance, which provides less advance notice of failure; time- or cycle-based preventive maintenance, which is inefficient compared with higher-level models; and reactive maintenance, which occurs after failure. There is still a place for each of these approaches for certain assets that are not critical to operations or safety, Rio notes.

Peter Reynolds, contributing analyst at ARC Advisory Group, notes that organizations that shift critical assets to prescriptive approaches are seeing significant improvements in maintenance costs, service costs, plant availability, and worker efficiency. 

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  • I would like to throw in my humble opinion about the above four types of maintenance answering the four questions: What; Why; When (What will happen); and How (to fix it). I would also like to add the fifth question: Where it will happen?. I think as it documented in the article that by using the historian and real time data ( Big data) and manuals and maintenance records we would be able to answer the What; Why; When and How? I think by using all of the above information and by adding also a performance model for the process and the equipment and using the design fundamentals and principles of the process and the equipment we might be able to develop an Artificial Intelligent (AI) model that would be able to answer the two questions When & Where when it comes to equipment failure and unexpected turndown. I think with a holistic approach for Data Management with Models based on big data, real time performance data and fundamental-based simulation and design models for the process and the equipment we would be able to develop an Eco- AI model for the whole plant to answer all the What; Why; When; Where; and How? questions and the advantages and cost saving that go with them as reported in the article.

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