Machine learning-based scheme for multi-class fault detection in turbine engine disks

Fault detection of rotating engine components in the aircraft engine is a challenging task that must constantly be monitored to provide aviation safety. In this paper, we propose a novel approach based on multi-layer perceptron (MLP) to detect in real time the degree of faults in a turbine engine disk due to a crack. To further improve detection accuracy while reducing computational complexity, the recursive feature elimination (RFE) is applied as a potent feature selection method. Satisfactorily, simulation results show that the proposed framework is robust to changes in operating conditions and outperforms comparative approaches.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.