EdgeCog: A Real-Time Bearing Fault Diagnosis System Based on Lightweight Edge Computing

Deep learning has made important contributions to classification tasks applied to fault diagnosis. However, it is crucial to integrate the technologies into real industrial applications through cost-effective hardware. Edge computing, a new computing paradigm, has the potential to accelerate system response time, reduce bandwidth for transmission, and use fewer computing resources. In this article, the distillation quantization compression method based on energy entropy is applied to compress the convolutional neural network (CNN), which is deployed on a Cortex-M4 series microcontroller with only 192 kB of RAM and 512 kB of ROM. Additionally, based on the fault mechanism of rolling bearings, this article integrates the attention mechanism and envelope spectrum to verify the effectiveness of feature extraction by the CNN model, which effectively weakens invalid features in the distillation quantization process. The experimental results show that the proposed method has excellent performance in terms of memory overhead and inference speed, which has great potential in industrial applications.