Potential failure prediction of ammunition feeding system based on improved depth belief network

This paper proposes an improved method for DBN, by means of introducing the detachment rate. The introduction of detachment rate can play a similar average role, and can make the complex relationship between the neurons weakened, so that DBN learning has stronger robustness. Three kinds of data (corresponding to healthy, faulted and deteriorating) were classified by the improved depth belief network (DBN). The results showed that the improved DBN was better than BP neural network and traditional DBN, this was expressed in accuracy, cross entropy and receiver operating characteristic curve. The classification accuracy rate was 93.9%.