Term of Award

Spring 2024

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Manufacturing Engineering

Committee Chair

Hossein Taheri

Committee Member 1

Jongyeop Kim

Committee Member 2

Meenalosini Vimal Cruz

Abstract

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise real-time, and reliable analysis are essential. Deep learning (DL) based data-driven algorithms could be a valuable solution to identify patterns and behaviors that might not appear by manual inspections of rail infrastructures. On the other hand, due to the wide variety of challenges from weather interference, the on-site testing of the DAS-Fiber Optic-Railroad set-up has proven to be highly expensive. This research work proposes a novel DL algorithm using multiple datasets acquired from the High Tonnage Loop (HTL), facilities of MxV Rail in Pueblo, CO. In the end, a finite element analysis (FEA) is conducted to determine the effectiveness of fiber optic acoustic detection in various circumstances and to reduce the cost of on-site testing.

OCLC Number

1432735968

Research Data and Supplementary Material

No

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