ABB’s Ellipse Asset Performance Management (APM) solution (part of the company’s Digital Enterprise portfolio) has been enriched with strengthened prognostic capabilities.

The new functionality enables customers to more accurately forecast when their critical assets are likely to fail and model alternative planning pathways. The new prognostic capabilities are a result of ABB’s acquisition of the software company, Cassantec.

The advanced software analytics have been integrated into the ABB Ability Digital Enterprise portfolio and deliver value to ABB’s industrial customers spanning the power, transportation, telecommunications, natural-resources sectors and other asset-intensive industries.

The enhanced prognostic functionality has been gained through the integration of recently acquired software firm Cassantec’s analytics within the Digital Enterprise portfolio. New features include condition-based prognostic horizons and risk profiles alongside simulation capabilities that extends an operator’s monitoring and diagnostic capabilities into the future – from fleet level all the way to individual assets.

This drives business value by minimising downtime and avoiding asset failure, reducing maintenance and operational costs. In addition, the prognostics functions deliver scenario analysis functionality over explicit future time horizons, greatly enhancing a company’s ability to optimize asset maintenance and replacement plans.

“By embedding Cassantec’s powerful prognostics into our industry-leading Ellipse APM product, part of the company’s Digital Enterprise, we are strengthening the value proposition we offer to our customers as their partner of choice as they embark on their digitization journey,” says Massimo Danieli, head of ABB’s grid automation business line in the company’s Power Grids business.

“By deepening and expanding analytical insight into asset performance and optimization, our APM solution drives even more value for our customers from the board room to the field.”

Cassantec’s field-proven technology accurately predicted 99% of equipment malfunctions with a time horizon of as long as five years, and that this accuracy rate increases over time through the data-driven machine-learning capabilities of its software. For client operators of fossil-fueled power plants, for example, Cassantec can provide annual savings of millions of euros per gigawatt of generation capacity.

In addition to improving commercial strategies and reputation, an additional benefit of the prognostics is in retaining expert knowledge as the global industrial workforce matures.