Abstract
AbstractFault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots.
Funder
Key Program of NSFC-Guangdong Joint Funds
The project of science and technology plan of Guangdong Province
Basic and Applied Basic Research Foundation of Guangdong Province
Zhuhai Science and technology Plan
Science and Technology Research in key areas in Foshan
Publisher
Springer Science and Business Media LLC
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