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An Industry-driven, Project-based Learning Activity: System Identification based on Vibration Signals using Machine Learning

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Conference

ASEE Southeast Section Conference

Location

Arlington, Virginia

Publication Date

March 12, 2023

Start Date

March 12, 2023

End Date

March 14, 2023

Conference Session

Mechanical Engineering 1

Tagged Topics

Diversity and Professional Engineering Education Papers

Page Count

12

DOI

10.18260/1-2--44984

Permanent URL

https://peer.asee.org/44984

Download Count

36

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Paper Authors

biography

Nektaria Tryfona Virginia Polytechnic Institute and State University

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Dr. Nektaria Tryfona is a Collegiate Associate Professor at the Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University. She received her B.Eng. and Ph.D in Computer Engineering and Informatics from the Polytechnic School, University of Patras, Greece.
She has extensive experience on building data management and database solutions for large-scale systems in collaboration with industrial and governmental agencies, and academic partners. She has published her work in peer-reviewed international conferences and journals.

Her current research interests include data management, data valuation and AI and engineering education. Her teaching/mentoring activities focus on developing and offering classes in project-based learning environments as well as, advising and mentoring students working in industry-driven problems.

Before joining Virginia Tech, she was tenured Associate Professor at the Computer Science Department, Aalborg University, Denmark, a Senior R&D Engineer in industry and academic research centers in USA and Europe, and the founder and Director of DataLab, George Mason University.

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Sindhu Chava Virginia Tech

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I'm Sindhu Chava, currently pursuing a master's in computer engineering (concentration in Machine Learning) from Virginia Tech. Prior to VT, I worked for 3 years as Data Scientist and I enjoy building models that translate data points into business insights

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Ayush Dhar Virginia Polytechnic Institute and State University

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M.Eng. Computer Engineering

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Taimoor Qamar Virginia Tech

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Graduate student at Virginia Tech interested in Machine Learning, Embedded Design, Autonomy and Robotics. Interested in the educative engineering process which makes the acquisition of the knowledge and skills required in various engineering fields possible for everyone. Interested in learning about new educational systems/projects/courses/formats/etc. that facilitate the spread of engineering knowledge and skills.

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Daniel Newman The Boeing Company

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Daniel is currently an Advanced Technologist in Boeing Research & Technology. He leverages his subject matter expertise in mechanical systems to inform model development and evaluation for machine health monitoring applications. He holds a PhD in mechanical engineering from the Georgia Institute of Technology.

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Abstract

This paper highlights an industry-driven Project-Based Learning (PBL) activity focusing on developing and evaluating Machine Learning algorithms for anomaly detection in the vibration data in aircrafts to further contribute to the machinery health. The project aims at providing students with an experience similar to what they will face in their career field. Pivotal role in this transforming experience is students’ responsibility to initiate and hold periodic meetings with the Boeing Subject Matter Expert to share results, receive feedback and address comments, as well as with the Team Advisor to evaluate and ensure progress. Though this they (a) comprehend challenges and approaches when working with real world problems, (b) built the ability to communicate with external partners and understand the used language and terminology, (c) function in a team environment, sharing responsibility while being accountable. This report documents both the PBL methodology tailored to engineering activities, along with the engineering innovation outcome and lessons learned.

Tryfona, N., & Chava, S., & Dhar, A., & Qamar, T., & Newman, D. (2023, March), An Industry-driven, Project-based Learning Activity: System Identification based on Vibration Signals using Machine Learning Paper presented at ASEE Southeast Section Conference, Arlington, Virginia. 10.18260/1-2--44984

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