(56m) Let’s Talk: Iiot Enabled Testbeds & Big Data Analytics Applications for Smart-Manufacturing

Authors: 
Shah, D., Auburn University

LET’S
TALK: IIoT Enabled Testbeds & Big Data Analytics
Applications for Smart-Manufacturing

Abstract:

Candidate
summary:

·        
Creative engineer with 2 years of industrial
experience and 4+ years of research experience in IIoT applications, big data
analytics
, smart manufacturing, process modelling & optimization
resulting in 4 successful collaborative research projects & 7+
peer-reviewed publications.

·        
Strong
algorithm and programming experience in MATLAB,
python, Linux high performance computing environments and parallel computing evidenced by
numerous written public and private codes during execution of research
projects. Public codes are available on GitHub (link on poster).

·        
Recognized leader & Effective communicator demonstrated
by 10+ presentations at international / national conferences and through
dedicated service in diverse organizations.

Poster
summary:

Why IIoT & Big
data analytics?

They enables real-time (remote) monitoring and control,
predictive maintenance, etc.
These capabilities help improve plant
efficiencies by achieving better control, reducing off spec production,
reducing equipment failures and process down time, etc.

What are our contributions?

We’ve
made contributions to all aspects of smart manufacturing. From hardware to software,
from data collection, data management, to data analytics
. We
have demonstrated how to make standard unit operations smart by utilizing
noninvasive IIoT sensors, Raspberry Pi
microcontrollers, lightweight wireless communication protocols, systems engineering
enhanced machine learning algorithms, model based feature selection, data
mining etc.

Specific
contributions:

1.    
IIoT enabled industrial testbeds:

Every step involved in developing IIoT enabled testbeds
will be discussed. We have tested IIoT
temperature sensor, vibration/accelerometers sensor (both digital and analog),
near IR light sensor, IR camera, video camera etc.

Key discussion points:

-       Lab
setup, experimental design & IIoT data
characteristics & challenges.

-       Predictive modelling framework built
with brief description of data
compilation, data mining, and data analysis
procedures.

-       Novel
model based data filtering or
feature selection approach capable of selecting relevant information for
vibration datasets.

-      
Performance comparison of hierarchical linear modelling with neural networks (ANN & LSTM).

2.    
Statistics
pattern analysis (SPA) modelling for spectrum data:

We will discuss SPA based modelling approach
developed for spectrum data. Developed approach captures more information with
less computation ideal for smart-manufacturing applications.

Basic
Idea:

-      
Divide
spectrum in different intervals and calculate statistics for each.

-      
Identify
most relevant statistics and use as independent variables.

Scope:

-       Tested
on 4 public real world NIR, UV/Vis spectrum data from different industries.

-       Compared
with 4 other linear, non-linear regression approaches

Major Advantages:

-              
Better prediction accuracy with improved
robustness.

-              
Simpler model with few important variables.

-              
Incorporates non-linearity.

-              
Faster training and validation.

3.    
Statistics
pattern analysis (SPA) modelling for batch data:

Next a variant of SPA modelling approach is
presented for semiconductor batch
data. Proposed variant was proven to
have better performance
by comparing its performance with other approaches
used in semiconductor industry.

Proficient
with the following data analytics and modelling/optimization techniques:

Partial least squares
(PLS), Kernel-PLS, Synergy interval partial least squares, Artificial neural
networks, Long short term memory (LSTM) neural networks, Binary matrix PLS,
Principal component analysis, Time series analysis, Recursive PLS, Kalman filter, LASSO, Bayesian optimization, Gaussian
process, Data filtering, Variable selection, Lomb’s Algorithm, Fourier
analysis, Signal processing.

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Individuals

AIChE Members $150.00
Employees of CCPS Member Companies $150.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $225.00