CBM: Then and Now

Overview

Let’s take a look at the evolution of “predictive” maintenance as a best practice to ensure smooth operations and longevity of your equipment. For the purpose of this blog article, condition-based maintenance (CBMCondition Based Maintenance) is defined as: a maintenance methodology in which maintenance operations are performed depending on the current condition of your assets. However, this was not always common practice at most manufacturing facilities.

How It Was Then…

Going back to the 1990s, computerized maintenance management systems were beginning to emerge. Many companies welcomed this solution as a way to solve their equipment failures and reduce maintenance costs. However, the technology alone did not resolve these issues, nor did it address the “reactive” nature of most maintenance programs. Stuck in the reactive mindset of the past, no amount of planningDetermination of resources needed and the development of anticipated actions necessary to perform a scheduled major job. An orderly appraisal and guarantee of all the prerequisites necessary to ensure completion of a given job at a predetermined time. It covers availability of ordered equipment, stores, materials, production, shutdowns, sketches, prints, specifications, etc. or scheduling seemed to work. And assessing the health of the equipment was not even on the radar. A totally new dynamic was needed to break the vicious cycle of failure, downtime, and expense.

How It Is Now…

The answer lies in the integration of all available technologies to achieve true CBM and predictive maintenanceThe use of instruments and analysis to determine equipment condition in order to predict failure before it takes place so corrective maintenance can be done in a planned and scheduled fashion. Examples include vibration analysis, oil analysis, thermography, airborne ultrasonic’s, NDT, motor current signature analysis, trending of process parameters, etc. of all types of equipment. This innovative, comprehensive approach was first introduced by Allied Reliability in the late 1990s into the early 2000s. It started with a proactive workflow model and a goal to do proactive work over-reactive work. We found that the more proactive work you do, the fewer failures you have. And the more production you have, the better service you provide to customers. Now, that’s a winning formula we can all agree on. The best finding was that with this approach, maintenance costs do come down!

How It Evolved

But things did not change overnight. Initially, the technology tools didn’t have the capability they have today…and they were extremely expensive. For example, a high-performance IR camera currently costs approximately $10,000; whereas, it used to cost between $75,000 and $80,000. So, the first hurdle was how to convince an organization to make the investment, which was especially challenging if dealing with non-engineering people and business leaders. It wasn’t only the sticker shock that had to be overcome; reactions ranged from skepticism that the technology didn’t work to thinking it was some sort of magic. Back then, people had to be educated and convinced to spend the money by demonstrating success fast—maybe not fast payback, but evidence that the technology worked. It took years to justify that new technology to support CBM methods was critical to monitor the health of equipment and stop expensive failures BEFORE they occurred.

Avoiding Pitfalls

With so many technological options on the market today, it can be confusing and difficult to choose which diagnostic device(s) are best suited to your needs. It’s easy to get distracted by all the shiny objects. Take wireless vibration sensors, for instance. There’s a myriad of sensors out there today, each with different features and capabilities. It seems like a new one is introduced just about every month. To prevent a costly mistake, it’s important to have a strong, basic foundation, such as a hierarchical list of all your critical assets, along with an understanding of how they fail. Once you have performed a thorough criticality assessment, you can capitalize on it to make strategic purchasing decisions. Regardless of the tool type, it should provide high-quality data on machine health, with a higher frequency than just once a month. Now, you can actually get readings every hour, or faster, if you want.

Conclusion

We have finally arrived at this moment with a proven, mature CBM model that firmly establishes the validity of predictive maintenance. The vast array of hardware and software technology to choose from makes it possible to cost-effectively and successfully integrate CBM into your reliability improvement process. In the long run, adopting this approach will allow you to have fewer analysts, who do more. It will also increase their bandwidth to focus on higher-level tasks thanks to CBM—in addition to other exciting advancements in cutting-edge techniques like artificial intelligence (AI) and machine learning (ML)—relieving them of simple, tedious tasks.

Ask an Allied Reliability representative how we can help you implement CBM into your equipment maintenance program.

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