Application of Edge Computing Based on MultiScale Convolutional Neural Networks in Gear Fault Diagnosis

Gear fault diagnosis plays a crucial role in detecting gear faults promptly, enabling timely repairs or replacements to minimize potential losses. While deep learning-based methods have been widely used for gear fault diagnosis, their effective application in real-world industrial environments remains a challenge. In this paper, we introduce a novel approach that combines a multi-scale convolutional neural network with multiknowledge distillation (MSCNN-MKD). This approach is deployed on edge computing nodes for online gear fault diagnosis. Experiments results validate the effectiveness of our proposed methodology. The proposed method achieves an accuracy of 98.76% in recognizing 7 fault categories. Additionally, the number of parameters is 18.54K, the floating-point operations are 41.54M, and the average inference time is 1.07ms. The methodology has great potential for practical industrial applications in gear faults diagnosis.