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
Authors of accepted papers will be expected to present their research at the workshop.

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

Important Dates

  • Paper Deadline: October 1, 2019 October 15, 2019
  • Author notification: November 1, 2019
  • Camera-Ready Submission: November 15, 2019
  • Workshop Day: December 9, 2019

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