Vibration Institute Webinar 4/6/2022

0.1 CEU Credit

TITLE: Building the Digital Twin for Balance of Plant Condition Monitoring

SPEAKER: Preston Johnson, Sr. Solution Manager - Asset Integrity & Reliability, CBT

ABSTRACT: We hear the term “Digital Twin” often in the context of the Industrial Internet of Things, Digital Transformation, or just going digital.  Digital Twins applications benefit asset intensive industries that own and operate many mechanical, electrical, and structural assets. Maintaining these assets at peak performance yields the highest revenue and the lowest operational costs.

There are several steps to prepare for and implement a digital twin for the benefit of our equipment, processes, safety, and of course plant ownership.  A well implemented digital twin yields return on investment in the areas of machine failure prevention, process optimization, and worker efficiencies both in the field and in the office. 

With the advent of low cost industrial IoT data collection tools, data collection becomes available daily, and is available to merge with operational data.  With synchronized asset monitoring, operational data, and advanced analytics, predictions of future maintenance and operational adjustments are made.

The Return-on-Investment from digital twin efforts comes in many forms.  The resulting optimization and efficiencies resulting from digital efforts include lower cost of maintenance, improved reliability of production, improved work execution and lower energy costs. 

This presentation shares the author’s roadmap for implementing industrial digital twins aimed at common plant maintenance and operations activities.  This presentation describes the journey from manual to an automated future at a chemical plant.  The presentation describes the asset and process modeling effort, the IoT tools for remotely monitoring the assets, and example analytics for improving maintenance and operations activities. 

Key takeaways:

• Asset modeling technique to determine what data is important
• Online monitoring tactics to collect needed data
• Levels of analytics to determine health and performance indicators
• Levels of analytics to predict future maintenance and operational needs
• System or organizational metrics and KPIs

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