[AISWorld] IEEE Big Data 2019 Call for Workshop: Predictive Maintenance Using AI

[Beth Hackett]

The IEEE Big Data 2019 Conference is accepting Workshop submissions on
the topic
of "Big Data Predictive Maintenance Using Artificial Intelligence."
<http://soc.southalabama.edu/bdpm2019/>

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 lostthroughput, 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 special session 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.

Proposed topics include:

   - Predictive maintenance using AI, Deep Learning, Machine Learning
   - Identification of fault diagnosis
   - Modelling & optimization of processes
   - Structural health monitoring, condition monitoring, & decision
support systems
   - Uncertainty based predictive maintenance
   - Time series based predictive maintenance
   - Soft computing for predictive maintenance
   - Predictive maintenance with live streaming data
   - Pre-processing & data analysis, characteristic fault features
   - Critical manufacturing & industrial system for predictive maintenance
   - Fault classification & feature selection for system diagnosis
   - Distributed computing of sub-system maintenance data using Neural
Networks and aggregating the results on the system level

October 15: Deadline for full workshop papers
November 1: Notification of paper acceptance to authors
November 15: Camera-ready deadline
December 9-12: Workshops

For more information, contact:
Ryan Benton rbenton at southalabama.edu
Rituparna Datta rdatta at southalabama.edu
Aviv Segev segev at southalabama.edu