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HoStB-DVGNN: A Flotation Fault Recognition Method Using Higher Order Spatial-Temporal Block and Dual-Stream

Ying Fan, Zhaohui Tang, Jin Luo

IEEE Transactions on Cybernetics

|May 18, 2026

Summary

This summary is machine-generated.

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This study introduces a novel method for recognizing fault patterns in froth flotation using higher-order spatial-temporal blocks and dual-stream variational graph neural networks (HoStB-DVGNNs). This approach enhances accuracy and robustness in identifying flotation process faults.

Area of Science:

Mineral Processing
Artificial Intelligence
Chemical Engineering

Background:

Accurate fault pattern recognition is vital for efficient froth flotation operations.
Dynamic flotation data present challenges like high dimensionality, nonlinearity, noise, and uncertainty.
Existing methods struggle with the complexity of flotation process data, impacting fault recognition accuracy.

Purpose of the Study:

To develop an advanced fault pattern recognition method for froth flotation.
To address the challenges posed by dynamic, complex flotation process data.
To improve the accuracy and robustness of fault detection in industrial flotation.

Main Methods:

Proposed a fault pattern recognition method using higher-order spatial-temporal blocks (HoStB) and dual-stream variational graph neural networks (DVGNNs).

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Constructed HoStB from key frame images to capture comprehensive flotation conditions.

Developed a DVGNN with apparent feature and HoStB streams for dynamic froth information extraction.

Implemented a bilateral self-supervision mechanism with a variational autoencoder (VAE) to boost generalization.

Main Results:

The HoStB-DVGNN method effectively extracts dynamic froth information.
The bilateral self-supervision mechanism significantly enhances model generalization performance.
Extensive experiments demonstrated the method's effectiveness and robustness on benchmark and real-world flotation data.

Conclusions:

The proposed HoStB-DVGNN method offers a robust solution for fault pattern recognition in froth flotation.
This approach successfully handles the high dimensionality, nonlinearity, and uncertainty of flotation process data.
The method validates its effectiveness and robustness in real-world industrial applications, reducing production risks.