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MAR-APR 2019

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INTECH MARCH/APRIL 2019 37 SPECIAL SECTION everything fails before replacing it. It is about finding the right balance be- tween taking action at the most oppor- tune time for each asset and allocating your resources in the right way. One way to manage the trove of data and insights you will get from condition monitoring is with a com- puterized maintenance management system (CMMS). This software main- tains a database of information on an organization's assets and maintenance operations. In addition to software like a CMMS, each organization should go through the process of performing a critical - ity analysis. This crucial assessment evaluates and classifies all of an opera - tion's assets to provide a clear under- standing of which require immediate attention, which can wait, and even which can run to failure. This evalua - tion will help clarify and inform your maintenance decisions and efforts. From a dollars and cents standpoint, the P-F curve applies more to critical assets. A criticality analysis helps you iden- tify how pivotal each asset is to the business. Think about it this way: You have two identical motors on different pieces of equipment, and they have two totally different rankings. That is because a criticality analysis consid - ers the impact an asset has on product quantity and quality, on the environ - ment, on safety, and on how that par- ticular machine affects everything else. When you do a criticality analysis, you are looking at every factor sur - rounding each asset and how integral they are to the process. Any time you start to analyze, you are likely to hear from your team that, "It's all critical. If I don't have any of these assets, then I can't produce products." It is not until you start breaking it down in de - tail that they start seeing how assets truly differ in importance. In general, assets are broken down into three different categories: A, high critical - ity; B, medium criticality; and C, low criticality. Typically, 10 to 20 percent of assets are highly critical. Between 20 to 30 per- cent of assets are in the medium range. And the rest, up to 40 to 60 percent, are low criticality. The criticality analysis helps you understand how each asset affects your operation, and that helps you see where you need to spend your time, money, and energy. The future of maintenance Emerging technologies, namely artifi- cial intelligence and machine learning, promise to take that kind of focus to the next level. With pattern recognition capabilities beyond human capacity, they will advance and enhance your maintenance operations. Data is one of the most valuable things in the world—if you know what to do with it. One of the biggest benefits of condition monitoring is that, as you learn exactly how your assets perform and fail over time, you will be able to sharpen and refine your maintenance program. And by taking advantage of emerging technologies, you will have extremely powerful software watch- ing for patterns and learning more every day. For a long time, maintenance was seen as just a cost to doing business. Now, with the concept of reliability engineering gaining traction and new technologies becoming more widely used, companies are recognizing ways to achieve operational excellence and gain strategic advantage. Maintenance can help companies carve out a com - petitive edge, especially once they have started using the P-F curve. n ABOUT THE AUTHOR Kevin Clark, CMRP (kevin.clark@fluke.com), vice president, Accelix, Fluke Digital Sys- tems, joined Fluke in December 2016. He has more than 30 years of industrial experience, working with both Fortune 500 and smaller start-up manufacturing/ technology companies in various leader- ship capacities. He currently maintains his conference board position with the Society of Maintenance & Reliability Pro- fessionals and is on the advisory board with Pumps & Systems. Clark has a BS, computer-integration in manufacturing, from Purdue University and an MBA from Colorado State University. View the online version at www.isa.org/intech/20190406. Identifying anomalies before they result in damage, down- time, or disruptive repairs decreases costs, unexpected downtime, and production loss.

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