W. Bartelmus and R. Zimroz, A new feature for monitoring the condition of gearboxes in non-stationary operating conditions, Mechanical Systems and Signal Processing, vol.23, issue.5, pp.1528-1534, 2009.
DOI : 10.1016/j.ymssp.2009.01.014

A. Bartkowiak and N. Evelpidou, Visualizing Some Multi-Class Erosion Data Using Kernel Methods, Proceedings in Computational Statistics, 17th Symposium Held in Rome, pp.805-812, 2006.

A. Bartkowiak and N. Evelpidou, Visualizing of some multi-class erosion data using GDA and supervised SOM, Biometrics, Computer Security Systems and Artificial Intelligence Applications, pp.13-22, 2006.

A. Bartkowiak, N. Evelpidou, and A. Vasilopoulos, Visualization of Five Erosion Risk Classes using Kernel Discriminants, Advances in Information Processing and Protection, pp.978-978, 2007.
DOI : 10.1007/978-0-387-73137-7_15

A. Bartkowiak and R. Zimroz, Outliers analysis and one class classification approach for planetary gearbox diagnosis, Journal of Physics: Conference Series, vol.305, issue.1, p.12031, 2011.
DOI : 10.1088/1742-6596/305/1/012031

G. Baudat and F. Anouar, Generalized Discriminant Analysis Using a Kernel Approach, Neural Computation, vol.25, issue.10, pp.2385-2404, 2000.
DOI : 10.1016/0893-6080(90)90049-Q

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1662

F. Camastra, Kernel Methods for Computer Vision, Theory and Applications

F. Camastra, Kernel Methods for unsupervided Learning, 2004.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference and Prediction, 2010.

A. K. Jardine, D. Lin, and D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, vol.20, issue.7, pp.1483-1510, 2006.
DOI : 10.1016/j.ymssp.2005.09.012

Z. Liu, J. Qu, M. J. Zuo, and H. B. Xu, Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis, The International Journal of Advanced Manufacturing Technology, vol.20, issue.8
DOI : 10.1007/s00170-012-4560-y

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, A. Smola et al., Constructing descriptive and discriminative nonlinear features: rayleigh coefficients in kernel feature spaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.623-628, 2001.
DOI : 10.1109/TPAMI.2003.1195996

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.2471

M. , K. Mika, S. Rätsch, G. Tsuda, and K. , Sch? olkopf B.: An introduction to kernel-based learning algorithms, IEEE Trans. on Neural Networks, vol.12, issue.2, pp.181-202, 2001.

J. Shawe-taylor, C. , and N. , Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

N. Trendafilov and K. Vines, Simple and interpretable discrimination, Computational Statistics & Data Analysis, vol.53, issue.4, pp.979-989, 2009.
DOI : 10.1016/j.csda.2008.11.018

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.325.1464

R. Zimroz and A. Bartkowiak, Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes, Journal of Physics: Conference Series, vol.305, issue.1, 2011.
DOI : 10.1088/1742-6596/305/1/012075

R. Zimroz and A. Bartkowiak, Multidimensional data analysis for condition monitoring: features selection and data classification, Electronic Proceedings, pp.11-14, 2012.

R. Zimroz and A. Bartkowiak, Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions, Mechanical Systems and Signal Processing, vol.38, issue.1, pp.237-247, 2013.
DOI : 10.1016/j.ymssp.2012.03.022