  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.
