SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine-learning-based models for the tasks of fault d...

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Bibliographic Details
Main Authors: Mailson Ribeiro Santos, Affonso Guedes, Ignacio Sanchez-Gendriz
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Machine Learning and Knowledge Extraction
Subjects:
explainable artificial intelligence
rolling element bearing
ML
fault detection and diagnosis
Online Access:https://www.mdpi.com/2504-4990/6/1/16

Internet

https://www.mdpi.com/2504-4990/6/1/16

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