Vibration signals-based bearing fault diagnosis using optimized multi-scale entropy and subtractive clustering

Auteurs :  aouabdi sali, BENDJAMA Hocine, boutasseta nadir
Année : 2018
Domaine : Electrotechnique
Type : Communication
Conférence: 2 nd international workshop on signal processing applied to rotating machinery diagnostics SIGPROMD'2018
Résumé en PDF :  (résumé en pdf)
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Mots clés : Fault Diagnosis, Vibration analysis, bearing faults, Algorithm SampEn and Subtractive Clustering.

Résumé : 

A new monitoring method based on multi-scale entropy (MSE) algorithm SampEn of vibration signals in conjunction with classification approach to detect mechanical- related faults in an experimental benchmark. The used of the classifier approach of the state in both cases of healthy and faulty scenarios with variable frequency. An experimental result indicates the degree of the importance of the choice of the features extraction for the classification application of faults.