Health Condition Prediction of Gears Using a Recurrent Neural Network Approach

The development of accurate health condition prediction approaches has been a key research topic in condition based maintenance (CBM) in recent years. However, current health condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. Neural network based methods have been considered to be a very promising category of methods for equipment health condition prediction. In this paper, we propose a neural network prediction model called extended recurrent neural network (ERNN). An ERNN based approach is developed for health condition prediction of gearboxes based on the vibration data collected from a gearbox experimental system. The results demonstrate the capability of the ERNN based approach for producing satisfactory health condition prediction results. A comparative study based on the gearbox experiment data further establishes ERNN as an effective recurrent neural network model for equipment health condition prediction.