Nonlinear statistics for bearing diagnosis

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 the recurrence plot, unlike the correlation dimension, are good at detecting a failure in a bearing. Additionally, we found that the Sample Entropy is exceptionally good at this task.