Refinery Gets Asset Failure Predictions with Nearly a Month of Lead Time

Because traditional diagnostic methods weren’t preventing equipment failures or identifying root causes of historic failures, a U.S. refinery turned to Aspen Mtell prescriptive maintenance to improve internal data science resources. Download this case study to learn how this refinery's pilot program with Aspen Mtell was able to predict failures with nearly one month of lead time, enabling planning for maintenance and rescheduling production.

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Industrial Data Fabric

Asset Performance Management

Digital Grid Management

Manufacturing and Supply Chain

Performance Engineering

Subsurface Science & Engineering

Refinery Gets Asset Failure Predictions with Nearly a Month of Lead Time

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