Presenter: Aaron BeazleyLearn from real examples how predictive maintenance can help your organization
Runtime: 27 minutes
Aging assets and infrastructure are one of the main factors in a project or process failing in any asset intensive industry. Predicting when and where a failure will occur is key in preventing downtime and reducing unplanned costs. Predictive maintenance, using a variety of advanced analytical techniques, is a way of spotting events or patterns in your asset data by automatically learning and improving from experience without being explicitly programmed.
Bentley Systems has been implementing Azure machine learning and other techniques into several industrial situations, with significant results.
In this first in a series of online Teck Talks, presented by Aaron Beazley, Asset Reliability Product Manager, will demonstrate how machine learning can provide further insights not only to the organization but also to their users with continuous intelligence and even automate decision making. Learn about:
- How machine learning can be applied to industrial situations, and the results they have providedThe advanced analytics techniques that were applied and how they were implemented
- The benefits and outcomes that were found in a short space of time
- How they can be applied
- How the results have changed processes since
The benefits to be gained from predictive maintenance are game-changing, but while there is a lot of talk, there seems to be very few real-world examples in industrial situations, and how they were applied.