Scalable bearing fault diagnosis using metaheuristic feature selection and machine learning for diverse operating conditions

Scalable bearing fault diagnosis using metaheuristic feature selection and machine learning for diverse operating conditions

Bearings are the foremost component of machinery that fails; hence early diagnosing mechanisms aided by artificial intelligence techniques play a crucial role. The objective of this study is to develop a scalable model for real-time bearing fault diagnosis using recent evolutionary algorithms such a...

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Bibliographic Details
Main Authors: B. R. Nayana, R. Subha, Rekha Radhakrishnan, P. Geethanjali
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Systems Science & Control Engineering
Subjects:
Bearing fault diagnosis
computational intelligence
feature selection
metaheuristic algorithm
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/21642583.2025.2469606
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https://www.tandfonline.com/doi/10.1080/21642583.2025.2469606

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