Zhou Zheng, Qiao Yusong, Lin Xusheng, Li Purui, Wu Nan, Yu Dong
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 Dec 24;25(1):9. doi: 10.3390/s25010009.
The rapid advancement of Industry 4.0 and intelligent manufacturing has elevated the demands for fault diagnosis in servo motors. Traditional diagnostic methods, which rely heavily on handcrafted features and expert knowledge, struggle to achieve efficient fault identification in complex industrial environments, particularly when faced with real-time performance and accuracy limitations. This paper proposes a novel fault diagnosis approach integrating multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms to address these challenges. Furthermore, the proposed method is optimized for deployment on resource-constrained edge devices through knowledge distillation and model quantization. This approach significantly reduces the computational complexity of the model while maintaining high diagnostic accuracy, making it well suited for edge nodes in industrial IoT scenarios. Experimental results demonstrate that the method achieves efficient and accurate servo motor fault diagnosis on edge devices with excellent accuracy and inference speed.
工业4.0和智能制造的快速发展提高了对伺服电机故障诊断的要求。传统的诊断方法严重依赖手工特征和专家知识，在复杂的工业环境中难以实现高效的故障识别，尤其是在面临实时性能和准确性限制时。本文提出了一种新颖的故障诊断方法，该方法集成了多尺度卷积神经网络（MSCNN）、长短期记忆网络（LSTM）和注意力机制来应对这些挑战。此外，通过知识蒸馏和模型量化，对所提出的方法进行了优化，以便在资源受限的边缘设备上部署。这种方法在保持高诊断准确性的同时，显著降低了模型的计算复杂度，使其非常适合工业物联网场景中的边缘节点。实验结果表明，该方法在边缘设备上实现了高效、准确的伺服电机故障诊断，具有出色的准确性和推理速度。
