(University of Eloued جامعة الوادي, 2022-01-24) Euldji, Riadh; Boumahdi, Mouloud; Bachene, Mourad; Euldji, Rafik
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
