Mahalanobis-ANOVA criterion for optimum feature subset selection in multi-class planetary gear fault diagnosis

The empirical analysis of a typical gear fault diagnosis of five different classes has been studied in this article. The analysis was used to develop novel feature selection criteria that provide an optimum feature subset over feature ranking genetic algorithms for improving the planetary gear fault...

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Veröffentlicht in:	Journal of vibration and control 2022-11, Vol.28 (21-22), p.3257-3268
Hauptverfasser:	Suresh, Setti, Naidu, VPS
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification Criteria Empirical analysis Fault diagnosis Feature extraction Feature selection Gear trains Genetic algorithms Machine learning Statistical analysis Support vector machines Variance analysis Vibration
Online-Zugang:	Volltext
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