DOI: 10.11799/ce202601005
Research on a Transformer-based multi-task cooperative monitoring architecture
NIU Yunpeng
SUO Zhiwen
WANG Huiwei
QU Bo
ZHOU Chaoyi
ZHANG Lifang
Abstract：The intelligent control of coal mines is facing the challenges of dynamic response lag and multi-source data fragmentation,while traditional models are incapable to capture transient anomalies underground and collaboratively analyze multimodal data.We propose a Multi-Task Adaptive Transformer Architecture(MTA-Transformer).Through cross-modal feature fusion and a shared encoder,it unifies the modeling of data such as equipment vibration and gas concentration,achieving multi-scale dynamic perception of the mining environment.This solves the problems of dynamic environmental monitoring and early risk warning.Experiments show that for bearing fault detection,the model achieves an accuracy of 93.5%,a False Alarm Rate(FAR)of 2.0%,and a response time within 5 ms,representing significant improvement over traditional models.For gas concentration prediction,it achieves a Normalized Root Mean Square Error(NRMSE)of 7.83%,a Prediction Interval Coverage Probability(PICP)of 91.7%,and enables early warnings up to 6 hours in advance.The MTA-Transformer provides a practical technical paradigm for the intelligent construction of mines.
Keywords：intelligent coal mine management and controlTransformermulti-task cooperationfault diagnosisgas concentration prediction
Publication Date：2026-01-20
Online Publishing Date：2026-03-10(First online date of this platform, not the publication date of the document)
Pages：8( 35-42 )