ALTASN: A few-shot learning fault diagnosis method for rotating machinery of unmanned surface vehicles based on attention mechanism

Abstract

Rotating machinery is one of the principal power equipment of unmanned surface vehicles (UVs). Considering the complex and harsh offshore working conditions of UVs, the health status of rotating machinery is highly to be affected, but it is often difficult to obtain enough fault samples. Accordingly, limited data fault diagnosis of rotating machinery holds great practical significance to increase the resilience and security of UVs. For limited data fault diagnosis, a novel few-shot learning model based on attention mechanism called adaptive long-term attention siamese network (ALTASN) is proposed. First, an efficient channel attention mechanism is combined with adaptive convolutional kernels to improve the spatial feature extraction capabilities of the convolutional neural network (CNN). To capture and assign higher weights to important long-term dependent information, long-term attention is introduced to improve the ability of long short-term memory networks (LSTM) temporal feature extraction. Finally, the siamese network is introduced to compare the features of different sample pairs to obtain the final fault type. In the case of limited data, the fault diagnosis performance and generalization ability of the proposed ALTASN are better compared with existing results. Experiments are carried out on the actual three-phase asynchronous motor experiment platform at the Zhejiang University of Technology to verify the effectiveness and generalization of the proposed method.