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Eugenie Technologies Private Limited

Save maintenance costs and GHG emissions from your heavy machinery with Eugenie’s end-to-end predictive maintenance suite. Eugenie goes beyond predictive maintenance and prescribes corrective measures to delay or avoid predicted equipment breakdowns.

Heavy machinery is vulnerable to unforeseen breakdowns. These breakdowns result in expensive downtimes and reliance on backup machines that increase the overall carbon footprint. By accurately predicting machine breakdowns in advance and suggesting corrective measures, Eugenie reduces unscheduled downtimes and reliance on backups.

SECTOR
Process & Batch Manufacturing, Discrete Manufacturing, Utilities
SOLUTION AREA
Machine Condition Monitoring, Environmental Monitoring
TECHNOLOGY
Artificial Intelligence
ALLIANCE RATING

Solutions

Solution 1

Ray-Finn

Ray-Finn is a data integrity assurance tool that ensures that any inputs from sensors or SCADA systems are as expected. Because if the sensors themselves don’t function correctly and pick the accurate data, the AI model won’t be able to make an accurate prediction.

Solution 2

Papillon

Papillon takes the high-integrity data from Ray-Finn and uses it to predict machine failures days, weeks, or months in advance. Built on an Explainable AI framework, Papillon tracks the performance of heavy machinery assets in real-time to predict future failures and present findings in an easy-to-understand manner. When a future failure is deemed probable, Papillon also shows the root cause of such a prediction, inspiring the human decision-maker's confidence in Papillon’s recommendation, and even prescribes next steps with evidences so that the operations team can take to reduce the likelihood of the failure.

Solution 3

Case Study

USA’s largest oil major leveraged Eugenie to risk-profile 3000 connected assets to identify the ones most at-risk of unforeseen failures, to concentrate maintenance optimizations on these high-risk assets. This improved the company’s OT teams’ adoption of analytics, while reducing their overall maintenance OpEx by 15%.

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15.0

% reduction in OpEx

85.0

Percentage reduction in data-to-decision times

8

in Million USD Savings in the first year

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