Driven by the increasing degree of automation, industrial plants, such as power plants, process plants, or discrete manufacturing plants, have become very IT and data intensive. The amount of data that is being processed and stored in automation systems today is immense. This poses a great opportunity for AI and machine learning in the industrial domain. Possible AI use cases are numerous and can include, e.g., plant equipment condition monitoring and predictive maintenance with the help of anomaly detection models, operator decision support systems with the help of plant behavior prediction models, or production energy optimization with the help of energy forecasting models. However, an effective and value-adding provision of such AI use cases needs to be achieved for users, e.g., by addressing ML research areas such as MLOps or Explainable AI (XAI). The objective of this industry track workshop is to provide a forum for data scientists and researchers as well as industrial practitioners to discuss and exchange the potentials, applications, and lessons learned of AI and machine learning in the industrial domain. The workshop program will be included as part of the IEEE BigDataService 2022 program.
This workshop program will be included as a part of the IEEE BigDataService 2022 program. This year’s conference is scheduled to take place in the San Francisco Bay Area, from 22-25 August 2022. The topics include, but are not limited to, the following:
- Industry 4.0, industrial big data, industrial digitalization
- Industrial automation systems, industrial control systems
- Failure detection and prediction, anomaly detection, failure root-cause analysis, predictive maintenance, condition monitoring
- Plant prediction models, prediction of critical plant situations
- Explainable AI (XAI) in the industry, UX, and human interaction
- Intelligent operator support assistants using NLP
- Search and knowledge discovery in industrial big data
- Continuous maintenance and improvement of deployed solutions
- Design of application-relevant datasets
- Case studies from design to deployment
All papers have to be submitted in PDF format through the BDS Easychair account, selecting the option: WORKSHOP – Industrial AI and Machine Learning.
A paper submitted at this forum is expected to be original research not previously published. A submission may not be concurrently submitted to another conference, workshop, or journal. The length of a camera-ready paper will be limited to 6 pages (IEEE Proceedings style) printed on 10-12 point fonts. Authors must follow IEEE Proceedings Author Guidelines to prepare papers with the template. At least one of the authors of each accepted paper is required to pay the full registration fee and present the paper at the workshop in person.
The accepted papers will be published as part of IEEE BigDataService 2022 Proceedings by IEEE CPS and included inside IEEE Xplore digital library. The workshop program will be included as a part of the IEEE BigDataService 2022 program. Selected Papers will be invited to submit to a journal special issue with 40% updates and enhancements.
The best papers will be recommended to several premium journals.
Full paper submission: June 10, 2022
Notification: June 26, 2022
Final Paper and Registration: July 10, 2022
Conference: August 15-18, 2022
Ajinkya Prabhune, SRH Hochschule Heidelberg, Germany (Main Chair)
Marcel Dix, ABB Corporate Research Center, Germany
Ashish Chouhan, SRH University Heidelberg, Germany
Swati Chandna, SRH Hochschule Heidelberg, Germany
Ojashree Bhandare, Magna Electronics Europe GmbH und Co KG, Germany
Sreeganesh Thottempudi, SRH Hochschule Berlin, Germany
Frank Schulz, SAP, Germany
Divya Sheel, ABB Corporate Research Center, India
Aldo Dagnino, ABB Group Information Systems, USA