TDNN: A novel transfer discriminant neural network for gear fault diagnosis of ammunition loading system manipulator

Abstract

The ammunition loading system manipulator is susceptible to gear failure due to high-frequency, heavy-load reciprocating motions and the absence of protective gear components. After a fault occurs, the distribution of fault characteristics under different loads is markedly inconsistent, and data is hard to label, which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions. To address these issues, the paper proposes a novel transfer discriminant neural network (TDNN) for gear fault diagnosis. Specifically, an optimized joint distribution adaptive mechanism (OJDA) is designed to solve the distribution alignment problem between two domains. To improve the classification effect within the domain and the feature recognition capability for a few labeled data, metric learning is introduced to distinguish features from different fault categories. In addition, TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result. The proposed TDNN is verified in the experimental data set of the artillery manipulator device, and the diagnosis can achieve 99.5%, significantly outperforming other traditional adaptation methods.