A Hybrid Intelligent Fault Diagnosis Framework for Rolling Bearings and Gears Based on BAYES-ICEEMDAN-SNR Feature Enhancement and ITOC-LSSVM

To address the challenges of difficult feature extraction for rolling bearing vibration signals, low efficiency in optimizing diagnostic model parameters, and the tendency to get trapped in local optima, this paper proposes an improved ICEEMDAN feature extraction method based on Bayesian optimizatio...

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Detalhes bibliográficos

Principais autores:	Xiaoxu He, Xingwei Ge, Zhe Wu, Qiang Zhang, Yiying Yang, Yachao Cao
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-02-01
coleção:	Sensors
Assuntos:	improved Tornado Optimizer with Coriolis force with Coriolis force least squares support vector machine bearing fault diagnosis BAYES-ICEEMDAN-SNR
Acesso em linha:	https://www.mdpi.com/1424-8220/26/5/1543
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