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What is ShuffleNet for Power Equipment Fault Diagnosis about?

This research paper presents a novel method for diagnosing insulation and mechanical faults in power equipment using a ShuffleNet lightweight convolutional neural network (SLCNN). The proposed SLCNN model significantly outperforms existing models with recognition accuracies of 95.77% for insulation and 99.9% for mechanical faults while also reducing computational and storage costs, making it suitable for Ubiquitous Power Internet of Things (UPIoT) applications. The study highlights the challenges of diagnos

Author

miletaetf

Language

EN