Operational Industrial Sensor Data for Scalable Asset Management in Energy Systems
Sensor-driven maintenance and operations scheduling in energy systems revolves around coordinating fleet-level electricity production with sensing and asset monitoring to help support maintenance decisions and constrol asset loading.
What makes this setting intersting is the presence of unique interactions and dependencies among generation assets, which are typically driven by different physical phenoomena and complex constraints such as power flow, degradation and operational limits.
In this talk, we will present a unified framework that embeds predictive degration models pertaining to the energy assets within decision optimization models to jointly solve operations and maintenance in variety of energy system settings. We will demonstrate decision making and AI mechanisms to address challenges associated with uncertainty modeling, scalability and privacy. Using classic benchmarks from the IEEE community coupled with real-world sensor data, we will illustrate some of the considerable cost and reliability improvements relative to existing state-of-the art approaches.
Date and Time
Location
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Registration
- Date: 08 Apr 2025
- Time: 11:00 PM UTC to 01:00 AM UTC
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- Starts 25 March 2025 04:00 AM UTC
- Ends 08 April 2025 04:00 AM UTC
- No Admission Charge
Speakers
Murat Yildirim of Wayne State University
Operational Industrial Sensor Data for Scalable Asset Management in Energy Systems
Biography:
Murat Yildirim is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University. Prior to joining Wayne State, he worked as a postdoctoral fellow at Georgia Institute of Technology (2016-2018). He obtained a PhD degree in Industrial Engineering, MS Degree in Operations Research and BS degress in Electrical Engineering and Industrial Engineering from the Georgia Institute of Technology. Dr. Yildirim's research interest lies in advancing the integration of mathematical programming and data analytics in various application domains. Specifically, he focuses on the modeling and the computational challenges arising from the integration of real-time inferences generated by advanced data analytics and simulation into large-scale decison optimization models used for optimizing and controlling networked systems. To date, Dr. Yildirim's research has been supported through funding from NSF, DOE, Michigan Translational Research and Commercialization and Ford Motor Company.
Address:Wayne State University,
Agenda
7:00 pm - Introduction and Opening remarks
7:10 pm - Presentation starts
8:15 pm - Presentation ends
8:20 pm - Questions & Answers
8:50 pm - Closing remarks
9:00 pm - Meeting ends