Improve Reliability with Predictive Analytics: A Practitioner’s Perspective

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.