FREE copy of the Uptime Elements Implementation Guide once you subscribe to Reliability Weekly

IMC-2018 Learning Zone 27:28

by Ed Abbott, Woyshner Services Company

This presentation covers how Duke Energy's Monitoring & Diagnostic Center utilizes over 11,000 Advanced Pattern Recognition and Predictive Analytics software models to monitor the condition of over 40,000 MW (87%) of their power generation fleet. The focus of the presentation is to share key insights in terms of people, process and technology, and lessons learned from the 10+ year journey in successfully implementing the program.

Topics addressed will include:

• Evolution of Duke's monitoring and diagnostic center

• Case studies with showing predictive catches of asset failure

• How to develop and sustain management support of the program

• Maximizing reliability with digital transformation.

banner
80% of Reliabilityweb.com newsletter subscribers report finding something used to improve their jobs on a regular basis.
Subscribers get exclusive content. Just released...MRO Best Practices Special Report - a $399 value!
DOWNLOAD NOW
How IoT-Based Predictive Maintenance and Handheld Inspections Compare and Complement Each Other

How IoT-Based Predictive Maintenance and Handheld Inspections Compare and Complement Each Other

The manufacturing industry has been following a route-based monitoring approach for ages. Without question, AI and IoT has changed the way we look at condition monitoring and diagnostics.

Case Study: Overhead Crane Monitoring Using Wireless IIoT Sensors

Case Study: Overhead Crane Monitoring Using Wireless IIoT Sensors

Wireless vibration sensors have been around for quite some time and have traditionally been used on simple, constant speed machines.

Upcoming Events

August 8 - August 10, 2023

Maximo World 2023

View all Events