Robust-InTemp:基于对抗扰动和局部信息增强的进阀温度预测

Robust-InTemp:Inlet Valve Temperature Prediction Based on Adversarial Perturbation and Local Information Enhancement

预测进阀温度的变化趋势对阀冷系统的运行状态有重要参考价值.针对传统方法存在数据收集时间跨度大和传感器存在误差等问题,本文提出了一种基于对抗扰动和局部信息增强的进阀温度预测模型Robust-InTemp.具体来说,Robust-InTemp通过对原始数据添加基于规则的高斯噪声,并使用基于梯度的对抗训练方法(projected gradient descent,PGD),增强了模型的泛化能力和抵抗噪声干扰的鲁棒性.同时,引入相对位置编码、一维卷积以及门控线性单元(gated linear unit,GLU),以增强模型对局部特征的学习能力,从而提高预测准确性.实验结果表明,与多种基准模型相比,Robust-InTemp在预测性能和抗干扰能力方面均有明显优势,进一步的消融实验也验证了模型中各个组件的有效性.

Predicting the trend of inlet valve temperature changes provides significant references for the operating status of valve cooling systems.Since the traditional methods have problems such as a large time span of data collection and sensor deviation,this study proposes a Robust-InTemp prediction model for inlet valve temperature based on adversarial perturbation and local information enhancement.Specifically,Robust-InTemp enhances the model’s generalization ability and noise resistance robustness by adding rule-based Gaussian noise to the original data and employing projected gradient descent(PGD)for adversarial training.Meanwhile,relative positional encoding,one-dimensional convolution,and gated linear units(GLUs)are introduced to enhance the model’s ability to learn local features,thus improving prediction accuracy.Experimental results show that compared to various benchmark models,Robust-InTemp has clear advantages in predictive performance and anti-interference ability.Additionally,further ablation experiments validate the effectiveness of each component in the model.