Aspen Mtell^® Video Library

Introduction to Aspen Mtell

Learn how Aspen Mtell prescriptive maintenance uses Autonomous Agents to predict equipment failures, detect deviations from normal behavior and prescribe detailed actions to mitigate or solve future problems.

Aspen Mtell: What is an Agent?

Aspen Mtell 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 they safeguard equipment, people and the environment.

Better Agents Faster with Maestro for Aspen Mtell

Data analysis can get bogged down in identifying, selecting, and preparing data. These tasks consume 50% or more of the time spent doing analysis. Now, with Maestro for Aspen Mtell, you can automate much of that data preparation.

Agent Scalability with AspenTech APM 4.0

Unexpected disturbances and shutdowns severely impact the bottom line of any manufacturing facility. The AspenTech APM 4.0 portfolio uses agents that can be quickly and easily replicated across all plant assets to assure production and avoid unnecessary maintenance.

Preventing Process Breakdowns with Aspen Mtell

See how prescriptive maintenance prevents catastrophic failure of a centrifugal gas compressor. Operators can detect process irregularities earlier and more accurately with Aspen Mtell and get advice to avoid the process degradation that leads to failures.

Improve Decision Agility with Aspen Mtell and Aspen Fidelis™

Machine learning technology can now predict equipment failures weeks in advance. But how do you decide when and how to perform the repair to maximize productivity and minimize risk? AspenTech innovations can help.

Improving Resource Allocation and Maintenance Efficiency

Discover how the Aspen Mtell Alert Manager functionality provides a centralized interface to identify the cause of problems and prioritize the equipment criticality and failure mode severity for any alerts in queue through an interactive risk matrix.

Maximize Supply Chain Resiliency

Achieve resilient operations by evaluating optimized downtime scheduling options to minimize impact on production and order commitments. Leverage prescriptive maintenance and advanced scheduling optimization to make ideal downtime decisions.