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Where Are You on the Maintenance Modernization Spectrum?

As the Industry 4.0 digitization trend spreads its influence across global manufacturing environments, one of the areas poised to benefit the most is asset maintenance. Traditional maintenance strategies have lacked any real insight into the actual condition of an asset.  As a result, time and money have sometimes been wasted on unneeded maintenance investments.  However, disciplined maintenance practices are necessary to avoid unanticipated equipment failures. The potentially catastrophic financial impact of unplanned plant downtime is too high a risk for most organizations, especially in the age of 24×7 operations. Today, thanks to advances in the ability to capture, consolidate and analyze asset performance data, condition-based monitoring—combined with advanced analytics—can provide plant and facility managers unprecedented insight into critical asset behavior.

But how can industrial organizations take the leap forward and make the transition to this new asset management world?  A first step is to perform a self- assessment of where your organization falls on the current maintenance spectrum. In all likelihood, most industrial organizations practice one, or a combination of, the following asset maintenance methodologies:

  • Reactive – Often referred to as “run to failure”, this approach only deploys maintenance resources after a failure occurs. Though sometimes appropriate for small, non-critical assets, deploying such a strategy on high criticality assets can be costly and disruptive. In these cases, fixes are oftentimes urgent and downtime costs can quickly accelerate.
  • Preventive – This approach schedules maintenance based on pre-established calendar dates, regardless of whether the equipment really needs servicing. Preventive maintenance has been in use for decades and has proven to be effective, although costly. Industry 4.0 advancements, however, now make it possible for stakeholders to look beyond the preventive approach to more affordable, more data-driven maintenance strategies.
  • Condition-based – In this case, condition-based maintenance (CBM) monitors the actual condition of an asset and identifies the nature of maintenance required. CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure. Such an approach provides the dual advantage of cost savings and improved uptime.
  • Predictive – This approach utilizes advanced analytics to mitigate accelerated aging due to usage and challenging environmental conditions, and optimizes both productivity and Overall Equipment Effectiveness (OEE). In this process, analytical models are used to predict abnormal asset behavior and to enable corrective action long before a problem materializes.

Today, many organizations are moving beyond preventive maintenance and adopting more condition-based and predictive maintenance practices. However, the ability to undergo such a migration depends upon the ability to perform condition-based monitoring of critical assets.

Leading edge deployments are producing quantified benefits

As companies embrace trends such as Industry 4.0, they are beginning to implement more advanced maintenance strategies. New monitoring methods provide continuous insight into the actual condition of an asset where none existed in the past, and with much more precision.  As a result, maintenance and inventory costs are significantly reduced as the incidents of unplanned downtime are minimized.

Some organizations are actively deploying data-driven maintenance strategies today.  The University of Rochester, for example, upgraded its facility with data gathering sensors and a cloud-based asset monitoring service in order to better maintain a power infrastructure that spans 5 million square feet (464,500 square meters). Their innovative digital approach monitors the heartbeat of the institution, its electrical system, and cuts maintenance costs, improves power system reliability, and relieves overburdened staff. According to facilities staff, their updated asset management system produced a 20 to 1 return on investment. In two incidences alone, they saved nearly $1,000,000 through early discovery of pending transformer problems.

BASF, the largest chemical company in the world, decided to implement a cloud-based version of CBM in one of its manufacturing sites. The CBM service monitors electrical equipment to verify the health of a portfolio of 56 electrical distribution assets.  In addition to identifying pre-failure abnormalities in the equipment, the CBM service also generates a risk assessment criticality matrix which helps users to determine which assets are at a greater health risk relative to how critical they are to the process at hand. The online algorithm generates a specific health index for the equipment. The health index flags those areas that require further analysis so that corrective actions can be taken to eliminate possible failures.

Smart asset management tools enable data capture and analysis

As the quantity and quality of plant equipment data continues to increase, that value of performance data and its contribution to advanced maintenance strategies improves dramatically. Digital technologies leverage machine learning methodologies to continuously improve asset performance insight and operational performance thereby improving plant productivity and profitability.

Tools such as Schneider Electric’s EcoStruxure Asset Advisor bring a proactive approach to managing electrical distribution assets, combining IoT and cloud-based technologies with Schneider Electric’s experts and services for improved business continuity. New services offer the ability to anticipate and address issues before they become critical incidents, mitigating safety risks, avoiding unplanned downtime, operational losses and expensive maintenance interventions.

To learn more about how predictive maintenance can help reduce costs and improve uptime download our “Strategies for Maintaining Electrical Distribution Equipment” white paper.


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