A FPGA accelerator-based WDCNN for rolling bearing diagnosis

Hongxin Yang; Feiyun Xu; Chao Li

doi:10.1117/12.2652344

2 November 2022 A FPGA accelerator-based WDCNN for rolling bearing diagnosis

Hongxin Yang, Feiyun Xu, Chao Li

Proceedings Volume 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022); 123511G (2022) https://doi.org/10.1117/12.2652344
Event: International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 2022, Nanjing, China

Abstract

In industrial practice, deploying a neural network for the real-time diagnosis of rolling bearings is a challenging task. To address this problem, this paper proposes a method for deploying Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) on a field programmable gate array (FPGA). Vibration signals can be used to predict the bearing condition directly by the accelerator. Using high-level synthesis tools to optimize the accelerator, the prediction time of a sample (2,048 points) has been reduced to milliseconds. Our research results indicate that the accelerator can be used to accurately and rapidly predict the bearing state.

Citation Download Citation

Hongxin Yang, Feiyun Xu, and Chao Li "A FPGA accelerator-based WDCNN for rolling bearing diagnosis", Proc. SPIE 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 123511G (2 November 2022); https://doi.org/10.1117/12.2652344

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