(629f) Big Data and Iot: A Demonstration Testbed of Multi-Stage Centrifugal Pumping System

Authors: 
Shah, D., Auburn University
He, Q. P., Auburn University
Wang, J., Auburn University

Big
data and IoT: a demonstration testbed of multi-stage
centrifugal pumping system

Devarshi
Shah1, Q. Peter He1, and Jin Wang1

1Department
of Chemical Engineering, Auburn University, Auburn, AL, 36849, USA

Abstract:

A
new industrial revolution is happening to make current manufacturing systems
smarter, safer, and more efficient. 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. This
revolution is known as smart manufacturing [1] or Industry 4.0. 

Although
many general frameworks have been proposed for IoT
enabled systems for industrial application [1]-[3]. There is limited literature
on demonstrations or testbeds of such systems. In addition, there is a lack of systematic
study on the characteristics of IoT sensors and data
analytics challenges associated with IoT sensor data.
This study is an attempt to help fill this gap by exploring the characteristics
of IoT vibration sensors and show how IoT sensors and big data analytics can be used to develop
real time monitoring frameworks.

In
this work, we will introduce the design of an IoT
testbed using multi-stage centrifugal pumping system equipped with non-invasive
IoT vibration sensors as shown in Fig. 1. We will discuss
the system architecture and protocols designed for data collection, transmission,
and storage as shown in Fig. 2. We will also discuss the characteristics of IoT sensors and data analytics challenges associated with
data collected from these sensors. The main focus of this work is the
development of data-driven predictive models based on the vibration signals to
infer flowrate inside the pipe and rpm of the pump motor, and compare with experimental
measurements. Altogether, this study serves as a demonstration of how IoT sensors and big data analytics can be integrated and
utilized for real-time process monitoring.

Fig.
1 Actual setup of the testbed with five IoT vibration
sensors attached to the pumping system at five different locations

References:

[1]      J.
Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, “Smart manufacturing, manufacturing intelligence and
demand-dynamic performance,” Comput.
Chem. Eng.
, vol. 47, pp. 145–156, Dec. 2012.

[2]      G.
Fortino and P. Trunfio, Eds., Internet of
Things Based on Smart Objects
. Cham: Springer International Publishing,
2014.

[3]      H.
S. Kang, J. Y. Lee, S. Choi, H. Kim, J. H. Park, J. Y. Son, B. H. Kim, and S.
Do Noh, “Smart manufacturing: Past research, present findings, and future directions,” Int.
J. Precis. Eng. Manuf. Technol.
, vol. 3, no. 1, pp. 111–128, Jan. 2016.