Remaining useful life prediction for multi-sensor mechanical equipment based on self-attention mechanism network incorporating spatio-temporal convolution

Author:

Yang Xu12,Tang Lin1,Huang Jian12ORCID

Affiliation:

1. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

2. Shunde Innovation School, University of Science and Technology Beijing, Beijing, China

Abstract

Driven by the limitations of spatial feature extraction in graph learning methods of multi-sensor mechanism equipment, this paper proposes a spatio-temporal self-attention mechanism network (STCAN) that integrates spatial relationships and time series information to predict the remaining useful life (RUL). Firstly, a graph convolutional network (GCN) is applied to extract the spatial correlation characteristics and fused with the self-attention mechanism network to obtain the global and local spatial features. Subsequently, a dilated convolutional network (DCN) is integrated into the self-attention mechanism network, to extract the global and multi-step temporal features and mitigate long-term dependency issues. Finally, the extracted spatio-temporal features are used to predict the equipment’s RUL through fully connected layers. The experimental results demonstrate that STCAN outperforms some existing methods in terms of RUL prediction.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Guangdong Basic and Applied Basic Research Foundation

National Foreign Experts Program of China

Publisher

SAGE Publications

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