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

📝 Original Info

• Title: A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications
• ArXiv ID: 2504.04541
• Date: 2025-04-06
• Authors: ** 제공되지 않음 (논문에 명시된 저자 정보가 없습니다.) **

📝 Abstract

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.

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