On the extraction of rules in the identification of bearing defects in rotating machinery using decision tree

A methodology for the extraction of expert rules in the identification of bearing defects in rotating machinery is presented. Data sets are collected from signals measured by piezoelectric accelerometer fixed on bearings of an experimental set-up. Temporal and frequential analyses are then conducted to determine statistical parameters (crest factor (CF), kurtosis, root mean square) and spectrums (Fast Fourier Transform, envelope spectrum). The decision tree is then constructed by applying C4.5 algorithm on the dataset, and thus expert rules are established. The efficiency and applicability of expert rules over rules resulting from human experiments in rotating machinery maintenance is shown throughout the present study.