Managing Industrial Automation Software
When you have many assets to manage, including equipment and machines, you want to be able to monitor your assets to ensure that they function well and undergo routine and necessary maintenance on time. But what if you could predict with confidence before a failure occurs. Would that change how often you pulled down a machine for routine maintenance? Knowing that over 30% of failures occur after routine maintenance, many asset owners are looking for strategies to improve the decision criteria so that routine maintenance can be delayed to “off peak” production periods to optimise business profits by reducing operating costs.
By implementing EAM solutions along with embedded predictive analytics tools, asset owners can have the capability to monitor all essential assets and make decisions based on empirical operational evidence. By using Parasyn, an Aspentech APM (MTell) Partner, you can implement solutions that cover all your bases.
What are EAM Solutions?
Enterprise asset management (EAM) includes monitoring physical assets during the whole life span of the assets, including design, commission, maintenance, and decommissioning. By managing assets based on real time and historical performance metrics rather than a time based (calendar) schedule, asset managers can greatly improve the utilization of the assets, as well as the overall performance.
Predictive analytic solutions data repositories including SCADA, Process Historians and relational databases and utilise algorithms built for machine learning. More advanced applications use pattern recognition to predict future events. This approach helps determine in advance if early maintenance is required thus reducing unplanned downtime, or delaying scheduled events to off peak times. Having a forecast of failure may also influence decisions relating to decommissioning equipment earlier than expected.
Where are Aspentech APM (MTell) Solutions used?
Aspentech APM (MTell) Solutions are used for critical infrastructure including transport, water, oil & gas and refineries. The industry of application does not pose any limits in the use of machine learning, however the data quality and access to trusted data has a significant impact on ROI. We expect to see machine learning used in almost all industries as organisations catchup and learn how to use these technologies to stay ahead of the pack. With adaptive machine learning where models of plant operation are not known, Aspentech APM is the perfect solution. After the data source is connected to the machine learning engine, you don’t need to be a data scientist to use MTell. Talk Parasyn if you would like to make a difference to your asset performance using machine learning. Talk to Parasyn if you need help creating trusted data.
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