Yang Yang1, Yuhan Long1, Yijing Lin2, Zhipeng Gao1, Lanlan Rui1, Peng Yu1,3,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250
Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >
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