基于差分非平稳Transformer的液压支架立柱压力预测

Hydraulic support pressure prediction based on non-stationary differencing Transformer

液压支架立柱压力预测是回采工艺决策的重要依据,也是确保围岩稳定的基础信息之一。然而,液压支架立柱压力虽然具有一定的规律性,却无法用简单的数学模型进行预测;且在回采过程中,支架不接顶、顶板破碎、传感器检测误差等带来大量的随机噪声,使得压力数据劣化为非平稳时间序列,给压力的预测带来的很大的困难。本文在Transformer基础上,提出一种差分非平稳Transformer模型,在Transformer的编码器和解码器中分别引入差分归一化和反归一化操作,以提升序列的平稳性。同时,在Transformer中采用去平稳注意力机制,计算序列元素之间的关联关系,以增强模型的预测能力。在真实的煤矿支架立柱数据集上的对比实验表明,本文提出的差分非平稳Transformer的预测效果达到0.674,表现明显优于LSTM、Transformer和非平稳Transformer模型。

Hydraulic support pillar pressure prediction has been a pivotal basis for decision-making in the mining process.It has been one of the fundamental pieces of information for ensuring the stability of the surrounding rock.However,although the pressure of hydraulic support pillars followed certain patterns,it couldn’t be predicted using simple mathematical models.Additionally,during the mining process,issues such as the support detaching the roof,roof fragmentation,and sensor detection errors introduced a significant amount of random noise,turning the pressure data into a non-stationary time series.This significantly complicated the pressure prediction.Based on the Transformer model,this paper proposed a differencing non-stationary Transformer model,which introduced differencing normalization and de-normalization operations in the Transformer′s Encoder and Decoder,respectively,to enhance the stationarity of the series.At the same time,a de-stationary attention mechanism was deployed within the Transformer to calculate the correlations between sequence elements,which thereby enhanced the model′s predictive capabilities.Comparative experiments on a real coal mine support pillar dataset showed that the differencing non-stationary Transformer model proposed in this paper achieved a prediction performance of 0.674,which was significantly better than LSTM,Transformer,and non-stationary Transformer models.