ABNN: Adaptive-Gating Binary Neural Network With Dynamic Activation Quantization for Industrial Health Status Prediction

Complex industrial equipment plays a critical role in specific tasks within industrial edge scenarios. Predicting their health status accurately is essential to ensuring safety and reliability in the production process. However, real-world industrial edge scenarios often have limited resources and stringent real-time requirements, making it difficult to deploy high-precision deep learning models directly at the edge. To address this issue, this article proposes an efficient adaptive-gating binary neural network (ABNN). First, a trend-aware encoder (TAE) is proposed to optimize the binarization process of the input layer. Next, a learnable precision indicator (LPI) is proposed to adjust the inference precision level. Finally, an adaptive-gating convolution is proposed to improve the representational capabilities while maintaining the fitting ability without significantly increasing the computational cost. Additionally, a field-programmable gate array (FPGA) hardware accelerator is designed for the proposed network. ABNN achieves approximately a 7% improvement in accuracy and a 45% gain in efficiency compared to the baseline model.