Date of Completion
Spring 5-1-2024
Thesis Advisor(s)
Chao Hu
Honors Major
Mechanical Engineering
Disciplines
Data Science | Electro-Mechanical Systems | Manufacturing
Abstract
It is difficult to overstate the impact of artificial intelligence (AI) over the past decade. The rapid expansion of machine learning has stimulated a race to deploy AI in all facets of life, one such domain being machine health monitoring. There is no doubt that machine learning excels in prediction accuracy, but oftentimes, these models are cryptic and fail to provide valuable insight into their decisions. This paper presents an overview of a neural network and what it means to learn. Next, two distinct Explainable AI (XAI) techniques will be presented: Gradient Class Activation Mapping and SimplEx . Finally, these XAI methods are implemented on a bearing vibration dataset typically used for benchmarking machine learning algorithms for bearing fault classification. Both XAI methods are implemented and demonstrated to be effective tools, offering utility in fault classification tasks.
Recommended Citation
Mundiwala, Mohammad, "Implementation of Explainable AI for Bearing Fault Classification" (2024). Honors Scholar Theses. 998.
https://digitalcommons.lib.uconn.edu/srhonors_theses/998