Get early, accurate warnings of equipment failures to avoid unplanned downtime, and prescriptive guidance to mitigate or solve problems.

Early
Detection

Recognize patterns in operating data that predict degradation and impending failure – well before it happens.

High Accuracy with Fewer
False Alarms

Using precise failure pattern recognition, avoid the high rate of false positives common with model-based solutions.

Prescriptive
Maintenance

Optimize asset performance by identifying the earliest signs of impending failure and use that data to make decisions to mitigate the event.

Fast Time to
Deployment

Using low-touch machine learning, rapidly identify normal and abnormal behaviors to start protecting equipment within weeks


AspenMtell

Aspen Mtell prescriptive maintenance software stops machines from breaking down, makes them perform longer, reduces maintenance costs, and drives increased production.
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Aspen Mtell delivers the earliest, most accurate warning of equipment failures and prescribes detailed actions to mitigate or solve problems. Using machine learning, Aspen Mtell can recognize precise patterns in operating data that indicate degradation and impending failure—well before it happens. Aspen Mtell stops machines breaking down, makes them last longer, reduces maintenance costs and results in net increases in production output on any processes where it is installed.

With Aspen Mtell, you can accurately predict time-to-failure, including precisely WHEN a known failure will occur, HOW the failure will occur and WHAT to do about it, derived from prescriptive advice such as the exact Failure Code directly linked from the EAM system. Knowing the precise, multiple days’ or weeks’ lead time to a failure allows the end user to determine the exact action necessary (often through discussions between Operations, Maintenance, Technical and Planning/Scheduling Departments). Such prescriptive action enables the best remediation and timing decisions.



AspenMtell
Advantages


  • Prescriptive guidance Advantages
  • Potential failure avoidance
  • Low-touch machine learning
  • Equipment- and process-agnostic
  • Earlier detection of equipment wear
  • More accurate failure detection with fewer false alarms

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