Multivariate statistical methods for detection of spur gear faults
[ X ]
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sage Publications Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, multivariate statistical techniques are experimented for a spur gear system and a methodology is proposed. The approach is based on the analysis of multidimensional gear vibration data without any feature extradiction and data transformation. The scheme is performed using the vibration signals acquired from a lab-scale single stage gearbox in three dimensions of x, y and z directions. As a groundwork, multi-normality assumptions are established using homogeneity, autocorrelation, and univariate normality tests. The bi-dimensional frequency histograms are also plotted to show bi-normality for experimental gear data. Then, mean vectors and covariance matrices of conditions of good, worn, 1-tooth broken from wheel gear and 1-tooth broken from each pinion and wheel gear are estimated. To compare gear conditions statistically, multivariate analysis of variance is proposed and applied. Moreover, the single metric of Mahalanobis distances are calculated to classify unknown test samples, utilizing the maximum likelihood estimates. The numerical results indicate that multivariate statistical analysis techniques can be applied in early detection of spur gear faults, in which univariate tests fail.
Açıklama
Arslan, Hakan/0000-0002-2019-1882;
Anahtar Kelimeler
Gear faults, vibration signals, signal processing, multivariate statistical analysis
Kaynak
Proceedings Of The Institution Of Mechanical Engineers Part C-Journal Of Mechanical Engineering Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
229
Sayı
14
Künye
closedAccess
