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Presentation Type
Presentation
Full Name of Faculty Mentor
William Jones, Computing Sciences
Other Mentors
Nathan DeBardeleben, Los Alamos National Laboratory
Major
Computer Science
Presentation Abstract
Our funding sponsor, Los Alamos National Laboratory (LANL), is interested in automatic anomaly detection and classification applied to highly instrumented flight shock and vibrational data for the purpose of providing insight into operational safety. In this work, we apply well-known Machine Learning (ML) techniques to a publicly available motor vibrational data set that serves as a proxy to the actual LANL data. We successfully train a random forest to classify anomalous motor states using the dataset, and use this model to simulate real-time anomaly detection and event classification. Furthermore, we perform a suite of computational studies to determine optimal parametric settings for our framework and evaluate the cost-benefit of these parameters.
Location
Room 2 (BRTH 112)
Start Date
12-4-2022 3:20 PM
End Date
12-4-2022 3:40 PM
Disciplines
Computer Sciences
Recommended Citation
Przybylski, Nicklaus, "Classification of Shock and Vibrational Data Using Contemporary Machine Learning Techniques" (2022). Undergraduate Research Competition. 64.
https://digitalcommons.coastal.edu/ugrc/2022/fullconference/64
Classification of Shock and Vibrational Data Using Contemporary Machine Learning Techniques
Room 2 (BRTH 112)
Our funding sponsor, Los Alamos National Laboratory (LANL), is interested in automatic anomaly detection and classification applied to highly instrumented flight shock and vibrational data for the purpose of providing insight into operational safety. In this work, we apply well-known Machine Learning (ML) techniques to a publicly available motor vibrational data set that serves as a proxy to the actual LANL data. We successfully train a random forest to classify anomalous motor states using the dataset, and use this model to simulate real-time anomaly detection and event classification. Furthermore, we perform a suite of computational studies to determine optimal parametric settings for our framework and evaluate the cost-benefit of these parameters.