66856067498087113b5b511b Factorytalk Guardianai

Automation & IIoT

FactoryTalk Analytics GuardianAI Brings Machine Learning to Maintenance

July 3, 2024

A machine learning continuous condition-based monitoring maintenance suite predicts failures and manages downtime.

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Rockwell Automation’s FactoryTalk Analytics GuardianAI is a machine learning-based supervisory application for maintenance departments. The software provides maintenance engineers with the insight to understand the current condition of such assets as pumps, fans and blowers on the plant floor.

Using electrical signal data from variable frequency drives, the application establishes baselines of individual assets under typical operating conditions. If there is a deviation from normal operating conditions, a notice is issued.

Once deployed, the no-code solution learns and operates at the edge. The application enables OT personnel—maintenance engineers, controls engineers, machine operators and plant managers—to use the data to plan maintenance activities and intervals based on the highest risk of failure. When anomalies are detected, embedded expertise helps identify the most probable cause of failure and the associated risk, allowing maintenance professionals to investigate and plan the corrective response.

For example, FactoryTalk Analytics GuardianAI will provide early warnings of a list of potential motor failures, such as shaft misalignment, unbalance, loose structural mounting (soft foot), mechanical looseness, ball bearing fault, inner and outer race bearing fault, and more.

Rockwell Automation highlights that the software provides integration with PowerFlex 755, 755T and 6000T drives for easy setup and deep insight for key process applications.

The application works with single-drive and motor applications. It can learn process-centric issues and can adapt to other asset types as well.

About the Author

Rehana Begg | Editor-in-Chief, Machine Design

As Machine Design’s content lead, Rehana Begg is tasked with elevating the voice of the design and multi-disciplinary engineer in the face of digital transformation and engineering innovation. Begg has more than 24 years of editorial experience and has spent the past decade in the trenches of industrial manufacturing, focusing on new technologies, manufacturing innovation and business. Her B2B career has taken her from corporate boardrooms to plant floors and underground mining stopes, covering everything from automation & IIoT, robotics, mechanical design and additive manufacturing to plant operations, maintenance, reliability and continuous improvement. Begg holds an MBA, a Master of Journalism degree, and a BA (Hons.) in Political Science. She is committed to lifelong learning and feeds her passion for innovation in publishing, transparent science and clear communication by attending relevant conferences and seminars/workshops.

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