Using Predictive Analytics to Minimize Risk Associated with Aging Assets

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It’s common knowledge that an aging asset infrastructure is of major concern in the power industry. According to the U.S. Energy Information Association, 74 percent of all coal-fired capacity in the United States was 30 years old or older by the end of 2012. That infrastructure is even more stressed when you consider the growing populations and urbanization trends that demand increased generation capacity. In addition, most electric utilities face pressure to keep electricity costs low while delivering reliable power, which can lead to challenging budget constraints. Thus, operators, engineers and plant managers continually strive to make every plant’s operation and maintenance dollar stretch as far as possible.

While operating assets for as long as possible can be cost effective and efficient, the practice can have quite the opposite outcome without proper preparations. Aging equipment can contribute to outages, failures, downtime, higher costs, decreased efficiency and a number of other associated problems. Aging assets could also cause regulatory, environmental compliance and safety issues.

Effective maintenance is a critical component to ensuring that assets, plants and entire fleets continue to operate reliably for long periods of time. Plant personnel employ a combination of maintenance techniques depending on the criticality of each asset, and organizations that do not have a comprehensive maintenance strategy in place are putting the operation at risk. If a potential asset failure could result in significant damage, safety issues or power outages, a proactive maintenance approach is needed.

Predictive Maintenance (PdM) involves continuous monitoring of the health of equipment and comparing its state to a model that defines normal operation to detect subtle early warning signs of potential failure. PdM typically uses advanced pattern recognition and requires a predictive analytics solution for real-time information about equipment health. The insights from a predictive analytics solution like Schneider Electric’s Avantis PRiSM helps engineers and plant operators better determine when an aging asset can continue running as is, needs to be serviced or needs to be replaced.


When applying predictive maintenance strategies, utilities are able to make smarter decisions about when and where maintenance should be performed. These decisions are based on the criticality of the asset, the asset’s performance history and the goals of the plant managers. Predictive analytics solutions allow decision-makers to extend maintenance windows by delaying maintenance that may not be immediately necessary. Rather than completing maintenance exactly as suggested by the original equipment manufacturer, the maintenance could be performed during a more convenient and cost-effective time.

As the power infrastructure in the United States continues to age, it’s more important than ever to understand how and why an asset is performing the way it is in order to avoid costly failures. The amount of data available to engineers and plant personnel also continues to grow, creating opportunities to further improve plant reliability and efficiency. Through predictive analytics solutions, this information is being used to monitor the health and performance of equipment and prevent failure of older assets.

Read more in this Power Engineering magazine article.

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  • Venkata Karri

    9 years ago

    Does this product shows Overall Equipment Effectiveness (OEE) analysis and creates automated alerts or maintenance requests in case of equipment failures?

    • Candice Hudson

      9 years ago

      Hi Venkata,

      Generally speaking, yes – the ‘overall equipment effectiveness’ could be taken by looking at a roll-up of all of the models built for a particular piece of equipment, to see if any of them are in warning/alarm, and the status of the Overall Model Residual (OMR).

      PRiSM also has automatic notification capability, when set up, to notify users of models that go into alarm/warning conditions. The software does not automatically generate work orders at this time, but the predictive analytics process can be manually linked to a comprehensive CMMS program.

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