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...

Volledige beschrijving

Bibliografische gegevens

Hoofdauteurs:	Xiaoxu He, Xingwei Ge, Zhe Wu, Qiang Zhang, Yiying Yang, Yachao Cao
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2026-02-01
Reeks:	Sensors
Onderwerpen:	improved Tornado Optimizer with Coriolis force with Coriolis force least squares support vector machine bearing fault diagnosis BAYES-ICEEMDAN-SNR
Online toegang:	https://www.mdpi.com/1424-8220/26/5/1543
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!