A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications

Fuente: arXiv
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Autori principali: Dogga, Bharadwaj, Sathyan, Anoop, Cohen, Kelly
Natura: Preprint
Pubblicazione: 2025
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author Dogga, Bharadwaj
Sathyan, Anoop
Cohen, Kelly
author_facet Dogga, Bharadwaj
Sathyan, Anoop
Cohen, Kelly
contents Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform predictions across many dimensions. The cutting edge of small and medium applications do not necessarily maintain operational sensors and data acquisition with rising costs and diminishing profits. We propose a framework to make sensor maintenance priority decisions using a combination of SHAP, UMAP, Fuzzy C-means clustering. An aerospace jet engine dataset is used as a case study.
format Preprint
id arxiv_https___arxiv_org_abs_2504_04541
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications
Dogga, Bharadwaj
Sathyan, Anoop
Cohen, Kelly
Computational Engineering, Finance, and Science
Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform predictions across many dimensions. The cutting edge of small and medium applications do not necessarily maintain operational sensors and data acquisition with rising costs and diminishing profits. We propose a framework to make sensor maintenance priority decisions using a combination of SHAP, UMAP, Fuzzy C-means clustering. An aerospace jet engine dataset is used as a case study.
title A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications
topic Computational Engineering, Finance, and Science
url https://arxiv.org/abs/2504.04541