Diagnostic des défauts des machines tournantes par reconnaissance des formes

Abstract

Currently, work on the automation of vibration diagnosis using Pattern Recognition techniques (PR), is mainly based on indicators extracted from Time sampled Acceleration signals. There are other attractive alternatives such as those based on angle synchronized measurements, which can provide a considerable number of more relevant and diverse indicators and, thus, lead to better performance in gear fault classification. The diversity of angular measurements (Instantaneous Angular Speed, Transmission Error and angular sampled Acceleration) represents potential sources of relevant information in fault detection and diagnosis systems. These complementary measurements of existing signals or new relevant signals allow the construction of Feature Vector (FV) offering robust and effective classification methods even for non-stationary operating modes with variable running speed or load. In the operational phase diagnostic using PR techniques. We have built several FVs based on indicators derived from the angular techniques to compare them to the ones calculated from time signals, in order to improve the detection and identification of gear faults. The results obtained show the contribution and the superiority of angular indicators in increasing classification performances over time indicators, using different supervised classifiers