Ball bearing monitoring using decision-tree and adaptive neuro-fuzzy inference system

Abstract

This study aims to provide a methodology that relies on the combination of the following approaches: the decision tree, the neural network, and the fuzzy logic to monitor the evolution of bearing degradation. Data collected from the vibratory signals generated from the tests carried out on ball bearings mounted in an experimental fatigue platform, are used. The decision tree method is applied to select the most relevant monitoring indicator, which will be used to develop an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training and test data required for model development have been classified according to the following states: normal, abnormal, and dangerous. These were defined from two thresholds: alert threshold and danger threshold. Then, the ANFIS model is trained from the indicators selected by the decision tree to predict the behaviour of the bearing in operation. The results confirm the effectiveness of the proposed approach for monitoring the health of ball bearing