Time-frequency analysis based Deep Learning for gear fault classification
Résumé
Fault diagnosis of gearboxes has attracted increasing interest in recent decades due to their ubiquity and importance in the industry. Modern research trends focus on developing a diagnosis system that works automatically with the application of artificial intelligence. These studies have treated Deep Learning (DL) network as a “black box”, where any input data would produce good results. This work proposes a novel Transfer Learning (TL) method using the time-frequency representation of gear vibration signals, which enables more accurate classification in complex working conditions and clarifies the black box. Using fine-tuning techniques proposed in this paper requires only a limited data set while ensuring acceptable classification results. An experiment test rig within different gear faults and load conditions was set up to evaluate the algorithm’s effectiveness.