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This document presents the preliminary results of an ongoing study related to the use of nonlinear statistics for bearing diagnosis. In this study, we propose a methodology based on the K-nearest neighbor algorithm to test the ability of a group of nonlinear statistic to differentiate between vibration signals obtained from rotatory machines with bearings in good and in bad condition. Results showed that statistics such as Lempel-Ziv complexity, Sample Entropy, and others derived from thedoi:10.1109/isspa.2012.6310586 dblp:conf/isspa/GuarinOD12 fatcat:bnsyijambngmddfmr3cwn7wejy