Health Index Development for a Planetary Gearbox

Planetary gearboxes are widely used in machinery such as wind turbines and helicopters. To maximize their effectiveness over their lifecycle, condition monitoring is often used, and proper health indexes can be developed utilizing condition monitoring data. A reported study treats health index development for electric motors as a regression problem and uses feedforward neural network to find the relationship between condition monitoring data and the health index. However, the reported method assumed a fixed step size following sequential ordering to select the optimal features. To improve the reported method, this paper proposes a feature selection strategy using genetic algorithm. A new health index (HI) is developed for planetary gearboxes. Experiments have shown that the proposed HI outperforms the reported HI. The proposed HI can better reflect the comprehensive health condition of planetary gearboxes.