Deep Learning for Industrial Analytics Workshop
Monday, 2 October 2017
The Prognostics and Health Management Society Annual Conference is the premier meeting of experts on Prognostics, Diagnostics, and System Health Management. Building on the 2015 Conference Deep Learning for Feature Engineering tutorial, and the large number of papers on deep learning presented at the 2016 Conference, we are holding a dedicated one-day Deep Learning for Industrial Analytics Workshop at the 2017 Conference. This first-of-a-kind workshop brings together experts from the PHM community and the deep learning community and provide an ideal forum for technical interchange and exploring the transfer of deep learning techniques from other applications to PHM.
The past ten years have witnessed a revolution in machine learning, statistics, and hardware. Neural networks have risen from relative obscurity as a collection of innovative new techniques known as Deep Learning, and are achieving human-level performance in image recognition and game playing. Around the same time, a niche discipline of Industrial Analytics has emerged, characterized by predictive analytics and optimization for fleets of similar assets – e.g., aircraft engines, subsea oil pumps, computed tomography scanners. Papers describing both novel applications of the combination of these techniques and related theory are encouraged.
The workshop will consist of invited talks, panels with deep learning researchers, and technical papers.
We are soliciting papers describing innovative new work, and potential panelists and keynote speakers for a full-day workshop at the Conference.
Topics of interest include:
- Detection, Diagnostics & Prognostics Methods
- Deep Learning for time series data
- Adaptive Control & Fault Accommodation
- Autonomous Systems/Robotic Technologies
- Decision Support Systems
With applications in:
- Marine, Automotive, Aviation, Locomotive,
- Mining, Oil & Gas, Smart Grid,
- Smart Manufacturing, Wind Energy,
- Medical Equipment, etc.
- Paper Submission Due: 19 June 2017
- Paper Review Feedback: 14 July 2017
- Final Papers Due: 4 August 2017
- Neil Eklund, Workshop Chair
- José Celaya, Schlumberger
- Weizhong Yan, GE Research