工矿自动化2026，Vol.52Issue(4)：88-95,133,9.DOI:10.13272/j.issn.1671-251x.2025090090
采煤机电缆光纤单元形状还原算法研究
Shape reconstruction algorithm of fiber optic unit in shearer cable
摘要
Abstract
Embedding fiber optic units into traditional shearer cables and using fiber optic sensing technology to obtain curvature and shape information in real time is an optimal solution for the condition monitoring of shearer cables.Existing shape reconstruction methods of fiber optic units based on mathematical modeling have low computational efficiency and cannot meet the requirements of real-time monitoring,while data-driven methods have weak extrapolation capability and are difficult to adapt to complex and dynamic underground working conditions.To address this issue,a shape reconstruction algorithm of fiber optic unit in shearer cable based on Physics-Informed Neural Network(PINN)prediction was proposed.In the PINN model,a Residual Network(ResNet)integrated with an SEAttention mechanism was introduced to construct a ResNet-SEAttention-PINN model to predict the curvature components of the fiber optic unit.Based on the strain-curvature mapping relationship,the parallel transport frame was used to solve the centerline curve equation of the fiber optic unit to reconstruct its shape.Simulation results showed that the Mean Absolute Position Error(Mean APE)and the Maximum Absolute Position Error(Max APE)of the proposed algorithm based on the ResNet-SEAttention-PINN model were 0.269 5 and 0.776 7 mm,respectively,which were significantly better than those of comparison algorithms based on convolutional neural network,recurrent neural network,PINN,ResNet-PINN,and SEAttention-PINN models.A physical experiment was carried out using an adjustable-radius calibration frame and an RP3000 dynamic distributed fiber optic strain testing system,and the results showed that under practical working conditions,the algorithm still maintained high reconstruction accuracy,with a Mean APE of 0.312 5 mm.关键词
采煤机电缆/光纤传感/光纤单元形状还原/物理信息神经网络/平行运输框架Key words
shearer cable/fiber optic sensing/shape reconstruction of fiber optic unit/physics-informed neural network/parallel transport frame分类
矿业与冶金引用本文复制引用
赵丽娟,崔浩东,高峰,张天一,刘子峰,林国聪,刘敬,刘洋..采煤机电缆光纤单元形状还原算法研究[J].工矿自动化,2026,52(4):88-95,133,9.基金项目
国家自然科学基金资助项目(51674134) （51674134）
山东兖矿集团长龙电缆制造有限公司委托项目(24-2342). （24-2342）
