Big Data Predictive Maintenance using Artificial Intelligence
Scheduled maintenance plays a significant role in any product-based industry. As a result, the losses due to unscheduled maintenance are required to be minimized. The losses add immense financial burden to the manufacturers. The losses can occur due to loss of cycle time, cost of lost throughput, yield loss, rework, repair, and maintenance cost. Researchers and practitioners have developed a plethora of preventive maintenance techniques to determine the condition of in-service equipment in order to predict the schedule of maintenance. The predictive maintenance helps in downsizing unplanned shutdowns, thereby increasing equipment availability. Some other potential advantages include increased equipment life time, planned safety, optimal spare part handling, and few accidents with negative impact on environment, thus increasing total profit of the manufacturer.
The motivation of organizing this workshop at the 2019 IEEE International Conference on Big Data is to integrate the ideas of predictive maintenance using machine learning methods and data-driven optimization. Every industry has to work on predictive maintenance to rectify failure before it occurs. In this regard, the main topics of interest of this session are the developments and challenges in bringing the concepts of computer-integrated manufacturing and maintenance strategies. Big data analytics techniques are being applied in every sector including predictive maintenance.
Topics of interest to this workshop include (but are not limited to):
- Predictive maintenance using Artificial Intelligence techniques, Machine Learning and Deep Learning algorithms
- Identification of fault diagnosis
- Structural health monitoring, condition monitoring and decision support systems
- Modelling and optimization of processes
- Pre-processing and data analysis, characteristic fault features
- Critical manufacturing and industrial system for predictive maintenance
- Uncertainty based predictive maintenance
- Fault classification and feature selection for system diagnosis
- Time series based predictive maintenance
- Soft computing for predictive maintenance
- Predictive maintenance with live streaming data
- Distributed computing of sub-system maintenance data using Neural Networks and aggregating the results on the system level
Paper Format and Submission
- Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system.
- Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see Formatting Instructions below).
Formatting Instructions
- 8.5" x 11" (DOC, PDF)
- LaTex Formatting Macros
Important Dates
- Paper Deadline:
October 1, 2019October 15, 2019 - Author notification: November 1, 2019
- Camera-Ready Submission: November 15, 2019
- Workshop Day: December 9, 2019
Presentation Schedule
Time | Title | Authors |
---|---|---|
8:00-8:05 | Opening Remarks | |
8:05-8:30 | Machine Learning Use Cases for Smart Manufacturing KPIs | Sandeep Jeereddy, Ken Kennedy, Edward Duffy, Annie Walker, and Bennie Vorster |
8:30-8:55 | Forecasting cross-border power exchanges through an HVDC line using dynamic modeling | Sylvie Koziel, Patrik Hilber, Per Westerlund, and Ebrahim Shayesteh |
8:55-9:20 | Failing & !Falling (F&!F): Learning to Classify Accidents and Incidents in Aircraft Data | Jarrod Carson, Kane Hollingsworth, Rituparna Datta, and Aviv Segev |
9:20-9:55 | Data Imputation Method based on Programming by Example: APREP-S | Hiroko Nagashima and Yuka Kato |
9:55-:10:20 | Application of Machine Learning and Spatial Bootstrapping to Image Processing for Predictive Maintenance | Vikram Krishnamurthy, Kusha Nezafati, and Vikrant Singh |
10:20-10:40 | Coffee Break | |
10:40-11:05 | Self-supervised Multi-stage Estimation of Remaining Useful Life for Electric Drive Units | Ivan Melendez, Rolando Dölling, and Oliver Bringmann |
11:05-11:30 | Wind Turbine operational state prediction: towards featureless, end-to-end predictive maintenance | Adrian Stetco, Anees Mohammed, Siniša Djurović, Goran Nenadic, and John Keane |
11:30-11:55 | Prescriptive Equipment Maintenance: A Framework | Suresh Choubey, Ryan Benton, and Tom Johnsten |
11:55-12:20 | Spatiotemporal Real-Time Anomaly Detection for Supercomputing Systems | Qiao Kang, Ankit Agrawal, Alok Choudhary, Alex Sim, Kesheng Wu, Rajkumar Kettimuthu, Peter Beckman, Zhengchun Liu, and Wei-keng Liao |
12:20-12:30 | Closing Remarks |
- PDF version of schedule is forthcoming
Workshop Chairs
- Aviv Segev, School of Computing, University of South Alabama
- Rituparna Datta, School of Computing, University of South Alabama
- Ryan Benton, School of Computing, University of South Alabama
Program Committee
- Subhash C. Bagui, West Florida University
- Yoonsuck Choi, Samsung; TAMU
- Djellel Difallah, Center for Data Science, New York University
- Raju Gottumukkala, Informatics Research Institute, University of Louisiana at Lafayette; College of Engineering, University of Louisiana at Lafayette
- Jennifer Lavergne, Electrical Engineering and Computer Science, McNeese State University
- Kaustubh Kaluskar, Boeing
- Satya Katragadda, Informatics Research Institute, University of Louisiana at Lafayette
- Murali Pusala, AT&T