Ageing asset infrastructure is a major concern in the power industry, with growing populations and urbanisation trends demanding increased generation capacity. In addition most 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 rand 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.
Ageing equipment can contribute to outages, failures, downtime, higher costs, decreased efficiency and a number of other associated problems. Ageing assets can also cause regulatory, environmental compliance and safety issues.
Effective maintenance is a critical component of 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 organisations 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 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. Predictive maintenance 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 help engineers and plant operators better determine when an ageing 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 power infrastructure continues to age, it is 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.
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