Aspen Mtell is a condition monitoring solution that 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. This predictive maintenance solution executes very precise pattern recognition of changes in stream signals produced from sensors on and around the equipment being monitored. As a result, it can detect extremely early onset of degradation across all the streaming signals and also temporal distinctions; tiny changes in those signals offset by time. Machine learning algorithms recognize various operating and failure modes as very precise patterns to establish normal operating mode and identify abnormalities.

Aspen Mtell predicts an accurate time-to-failure indicating precisely WHEN a known failure will occur, HOW the failure will occur and WHAT to do about it. 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 timely decisions to completely avoid damage, prevent a breakdown, and/or solve the problem in the most efficient manner.

Aspen Mtell Advantages:

• Prescriptive guidance

• Potential failure avoidance

• Low-touch machine learning

• Equipment- and process-agnostic

• Earlier detection of equipment wear

• More accurate failure detection with fewer false alarms

Most condition monitoring products tend to rely on simulations rather than true patterns based on historical data. As sensors capture information about operating conditions, these products don’t analyze that data to change the patterns they seek – on their own, these tools do not correctly identify the behavioral signatures of normal equipment operation and exact failure. Consequently, other products only speculate on time-to-failure because they lack Aspen Mtell’s inherent precision.