Gear Tooth Fault Detection Based on Designed Convolutional Neural Networks

Gearbox is one of the most important parts of the rotating machinery, so health monitoring of the gearbox is essential. The accurate positioning of tooth failure of gear is an important function of the fault diagnosis system. This paper proposes a detection strategy based on designed convolutional neural networks to detect and locate gear tooth failure. The detection strategy aims to compare the characteristic gap between the normal gear and the faulty gear in the same period extracted by the convolutional neural network, and assign weights to the faulty gear vibration signal to obtain the weight sequence of the faulty vibration signal, so as to obtain the faulty tooth weight. Finally, the health condition of the gear can be evaluated by comparing the weight between all teeth of the gear. The proposed detection strategy is tested through simulation vibration signal and experiment vibration signal. The result shows that the proposed method can successfully identify gear failure and effectively detect single tooth failure on gear.