University of Illinois at Chicago
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Enhancing Safety in Locomotive Maintenance through Deep Learning: A CNN-LSTM Approach for Forecasting

thesis
posted on 2023-08-01, 00:00 authored by Brady J Middleton
This study presents the utilization of a deep learning model, specifically a multi-classification CNN-LSTM approach, to enhance safety in locomotive maintenance. The model achieved an impressive accuracy of 91.97422%, showcasing its potential to predict and prioritize maintenance issues accurately. Notable predictions include a high likelihood of bearing-related complications and battery performance problems. Further insights highlighted moderate likelihoods for issues concerning cooling systems, electrical components, and other engine parts. Additionally, the model provides a percentage distribution of prevalent issues across specific facilities, such as those in Chicago and Indiana. This highlights the immense potential of the CNN-LSTM model in regionalizing preventive measures, emphasizing its transformative capacity in locomotive maintenance. With continued refinement and application, we anticipate a safer and more efficient trajectory for locomotive maintenance endeavors.

History

Advisor

WILLIAMS, QUINTIN

Chair

WILLIAMS, QUINTIN

Department

Mechanical and Industrial Engineeirng

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Masters

Degree name

MS, Master of Science

Committee Member

DERRIBLE, SYBIL HUANG, JIDA

Submitted date

August 2023

Thesis type

application/pdf

Language

  • en

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