Artificial Intelligence for Predictive Maintenance
Updated September 20 with registration details
Registration for the event
AAAI handles registration for all 2022 Fall Symposia. Registration Link
Registration is open until the start of the event, but early registration is encouraged.
Please see AAAI’s Fall Symposia site for more details on registration and hotels/transportation.
Description of the Symposium
Complex, physical systems degrade over time, and continued maintenance is required to ensure their peak performance. Upkeep of well-engineered systems is often straightforward, yet significant challenges lie in knowing when and what to maintain. Traditional approaches to managing maintenance activities rely on scheduled or condition-based approaches. Predictive Maintenance (PMx) paradigm complements them with the ability to forecast needs into the future, reducing monetary and logistical burden of ownership, boosting operational safety, and reducing system down-time due to unexpected failures. Past successes show that PMx is capable of achieving those goals, however, there are remaining challenges and untapped opportunities in applying this technology at-scale in real-world settings. Many of these outstanding issues can be resolved with Artificial Intelligence.
This symposium aims to bring together researchers and practitioners across academia, industry, and government in order to discuss challenges, approaches, and needs in the field of AI-driven PMx, as well as fuel collaborative efforts which will accelerate the progress of adopting AI across entire organizations that maintain critical systems.
Topic Areas Include (but are not limited to)
- Readying maintenance, logistics, and/or systems data for AI
- Work on curating, cleaning, featurizing, annotating, and/or sketching complex data from critical systems.
- Analytical frameworks for critical system prognostics
- Work on defining predictive tasks and applying various modeling paradigms to support prognostics
- Impact assessments of AI-driven Predictive Maintenance
- Work on defining metrics of success and translating AI evaluation metrics (e.g., accuracy) into business and operational metrics (e.g., reduced down-time).
- Implementation Strategies
- Work on implementation of research and development in practice.
- Using AI to maintain peak operation of cyber-physical systems
The symposium will feature poster sessions along with sessions of talks, organized by topic area, to present submitted work. AI for PMx is intended to be an in-person event. More details will be posted here as they become available.
- Important Dates
- August 27th 11:59PM AoE (UTC -12): Submissions due
- September 11th: Authors notified
- September 22nd: Final papers due
- September 23rd: Registration for accepted papers
- November 15: Registration closes
- November 17-19th: Symposium takes place at Westin Arlington Gateway in Arlington, Virginia
- Submission Requirements
- Unpublished works
- Main Track: manuscripts up to a maximum of 6 pages in length plus one extra page for references.
- Student Track: manuscripts up to a maximum of 2 pages in length including references.
- All submissions must use the 2022 AAAI AuthorKit
- All submissions will be double-blind reviewed
- Submit papers via EasyChair
- Proceedings of all accepted Main Track papers will be published
- Submission date August 27th AoE
- Unpublished works
- Accepted Talks and Poster Sessions
- For both Main and Student Tracks, the Program Committee will decide whether each accepted submission will be scheduled for a poster or oral presentation (20 minutes including Q&A), or both.
- Keynote Speakers
- TBD speaker from academic sector
- TBD speaker from government sector
- TBD speaker from industrial sector
- Panel Discussions
- Future of Predictive Maintenance
- Lessons Learned in Predictive Maintenance
- PMx Data - Readiness for AI
- Student Panel
- Chair: Dr. Nicholas Gisolfi, Research Scientist, Auton Lab, Carnegie Mellon University
- Mr. David Alvord, Senior Research Engineer, Georgia Tech Research Institute
- Dr. Abdel-Moez Bayoumi, Professor of Mechanical Engineering, University of South Carolina
- Dr. Artur Dubrawski, Alumni Research Professor of Computer Science, Auton Lab, Carnegie Mellon University
- Dr. Stephen Robinson, Professor of Aerospace Engineering, University of California
- Co-Chair: Dr. Artur Dubrawski, Alumni Research Professor of Computer Science, Auton Lab, Carnegie Mellon University
- Co-Chair: Dr. Dragos Margineantu, AI Chief Technologist, Boeing Research & Technology