FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis Plate Contact Abnormality Detection

TL;DR

This paper introduces FM-AE, a multimodal autoencoder that fuses voltage signals and infrared images to accurately detect plate contact abnormalities in zinc electrolysis, enhancing production stability.

Contribution

The paper presents a novel frequency-masked multimodal autoencoder that effectively combines voltage and infrared data for real-time abnormality detection in zinc electrolysis.

Findings

Achieves 86.2% detection accuracy

Demonstrates robustness and generalization in various conditions

Provides effective support for industrial production

Abstract

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.