Technology That Loves Complexity
Get early and accurate warning of when an asset failure will occur, how the failure will occur and what to do about it.
Creating a World that Doesn't Break Down
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.
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, not months.
Aspen Mtell prescriptive maintenance software stops machines from breaking down, makes them perform longer, reduces maintenance costs, and drives increased production.
Advances in technology often do a great job of improving the bottom line. But what if this technology could also improve your quality of life? In this video, see how AspenTech delivers the technology ...
Case Study: Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence
As part of an effort to drive reliability in its refinery operations, Saras turned to Aspen Mtell prescriptive maintenance to improve equipment uptime and decrease maintenance costs. Learn how Saras e...
Aspen Mtell: What is an Agent?
Aspen Mtell is a prescriptive maintenance solution that uses Agents to recognize asset failures earlier and with greater accuracy. Learn what an Agent is, what they do, how they are created, and how t...
Aspen Mtell Brochure
Learn how Aspen Mtell uses machine learning to recognize precise patterns in operating data that indicate degradation and impending failure—well before it happens.
Digital Acceleration Opens a New Frontier of Value Creation
In asset-intensive industries, IIoT and digitalization technologies are paving the way to increased profitability and reliability. However, capturing this opportunity does not require a full-scale dig...
Remaining Useful Life: There’s More to It Than You Think
Remaining useful life (RUL) is a key metric for predictive maintenance applications. However, the concept is flawed.
Ramp up Reliability With Low-Touch Machine Learning for Hyper Compressor Monitoring
When hyper compressors fail, the cost of production losses can range from tens of thousands to millions of dollars per occurrence. In this white paper, learn how companies are using Aspen Mtell to rec...