(169c) Statistics Pattern-Based Big Data Analytics Framework for Iot-Enabled Cybermanufacturing

Statistics
pattern-based big data analytics framework for IoT-enabled cybermanufacturing

Jin Wang¹,
Q. Peter He², Anthony Skjellum³, Devarshi Shah¹,
Carlos Lemus³

¹Department of Chemical Engineering, Auburn
University, AL 36849

²Department of Chemical Engineering, Tuskegee
University, AL 36088

³Department
of Computer Science and Software Engineering, Auburn University, AL 36849

With the emergence of the Industrial Internet of Things
(IoT) and ever advancing computing power and expansion of wireless networking
technologies, a new generation of networked, information-based technologies,
data analytics, and predictive modeling are providing unprecedented embedded
computing capabilities as well as access to previously unimagined potential
uses of data and information. These capabilities provide possibilities for new,
radically better ways of doing manufacturing. Although there are different
names used to describe next generation manufacturing systems, such as
cybermanufacturing and smart/advanced manufacturing, the essence of these is
the application of increasingly powerful and low-cost computation and networked
information-based technologies in manufacturing enterprises. There is a general
consensus that factories and plants connected to the Internet are more
efficient, productive and smarter than their non-connected counterparts.

Manufacturing process operation databases are massive
because of the use of process operation and control computers and information
systems. With ever-accelerating advancement of IoT devices and other
communication and sensing devices and technologies, it is expected that the
data generated from cybermanufacturing systems will grow exponentially. 4 V's are
often used to characterize the essence of big data: Volume (from terabytes (~10¹²)
to zettabytes (~10²¹)), Variety (from structured to unstructured), Velocity
(from batch to online streaming), and Veracity (from well calibrated and
cleansed data to less trustworthy and uncleansed data).

Big Data is arguably a major focus for the next round of
the transformation of advanced manufacturing. According to research by McKinsey
Global Institute and McKinsey's Business Technology Office, the analysis of
large data sets will become a key basis of competitiveness, productivity
growth, and innovation.

In a newly funded NSF EAGER project, we propose a novel, statistics
pattern-based process data analytics framework with the aim to provide smart
diagnostics and prognostics for cybermanufacturing. As part of this effort, we
also propose to establish an IoT-enabled manufacturing technology testbed (MTT)
to explore and establish a proof-of-concept for the proposed framework. The
theoretical background of the framework, the setup and test of the MTT, as well
as the preliminary results obtained will be presented and discussed.