Zinc electrolysis is one of the key processes in zinc smelting, and maintaining stable operation of zinc electrolysis is an important factor in ensuring production efficiency and product quality. However, poor contact between the zinc electrolysis cathode and the anode is a common problem that leads to reduced production efficiency and damage to the electrolysis cell. Therefore, online monitoring of the contact status of the plates is crucial for ensuring production quality and efficiency. To address this issue, we propose an end-to-end network, the Frequency-masked Multimodal Autoencoder (FM-AE). This method takes the cell voltage signal and infrared image information as input, and through automatic encoding, fuses the two features together and predicts the poor contact status of the plates through a cascaded detector. Experimental results show that the proposed method maintains high accuracy (86.2%) while having good robustness and generalization ability, effectively detecting poor contact status of the zinc electrolysis cell, providing strong support for production practice.

翻译：摘要：锌电解是锌冶炼的关键工艺之一，维持其稳定运行是保障生产效率与产品质量的重要因素。然而，锌电解阴极与阳极之间的接触不良是导致生产效率下降及电解槽损坏的常见问题。因此，在线监测极板接触状态对确保生产质量与效率至关重要。针对该问题，本文提出一种端到端网络——频率掩蔽多模态自编码器（FM-AE）。该方法以电解槽电压信号和红外图像信息为输入，通过自动编码融合两种特征，并利用级联检测器预测极板接触不良状态。实验结果表明，所提方法在保持高准确率（86.2%）的同时具有良好鲁棒性与泛化能力，可有效检测锌电解槽接触不良状态，为生产实践提供有力支撑。