| 梁楠,高世伟,张伟,田添,薛瑞争.基于融合相关性的协同分摊噪声软测量建模[J].电子测量与仪器学报,2025,39(9):172-181 |
| 基于融合相关性的协同分摊噪声软测量建模 |
| Fused correlation-based collaborative shared noise soft-sensing modeling |
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| DOI： |
| 中文关键词: 数据驱动建模 软测量 融合相关性 协同分摊噪声 HCAN-CNN |
| 英文关键词:data-driven modeling soft sensor fused correlation integrated collaborative allocation of noise HCAN-CNN |
| 基金项目:河南省科技研发计划联合基金（235200810049)、河南省科技攻关项目（232102210056，242400410545）、河南省科学院高水平成果奖励与培育项目（20252307001，242207026）、河南省科学院基本科研业务费项目（220607053、20250607002）资助 |
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| Author | Institution |
| Liang Nan | Institute of Applied Physics, Henan Academy of Sciences, Zhengzhou 450000,China |
| Gao Shiwei | School of Computer Science and
Engineering,Northwest Normal University, Lanzhou 730000,China |
| Zhang Wei | Institute of Applied Physics, Henan Academy of Sciences, Zhengzhou 450000,China |
| Tian Tian | Henan Technical College of Construction, Zhengzhou
450000,China |
| Xue Ruizheng | Henan Guojian Medical Equipment Co., Ltd., Shangqiu 476002,China |
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| 中文摘要: |
| 基于数据驱动的软测量建模方法在流程工业中有着广泛的应用。流程工业中，辅助数据常常会受到异构、杂糅的噪声的污染，且工业数据中线性相关与非线性相关共存，而噪声问题和不合理的相关关系表达均会严重影响软测量模型的预测结果。在协同分摊噪声算法的基础上提出一种基于融合相关性的协同分摊噪声算法进行软测量建模。首先，采用融合了关注线性相关性的Pearson系数和关注非线性相关性的Spearman系数的融合相关性系数优化协同分摊噪声算法，使协同分摊噪声算法中数据可信度计算更合理，更符合工业数据中线性相关与非线性相关共存的情况。然后，结合卷积神经网络（convolutional neural networks，CNN）搭建软测量模型。在脱丁烷塔数据集上进行多降噪方法、多模型和多回归方法的交叉组合实验，结果表明，该优化后的降噪算法较基础的协同分摊噪声算法、小波变换降噪、降噪自编码器有着较强的降噪能力；所搭建的软测量模型有着较优的预测精度及较小的预测误差，其中决定系数（r-square，R2）指标和均方误差（mean squared error，MSE）分别为0.971 6和0.001 1。 |
| 英文摘要: |
| Data-driven soft-sensing modeling plays a critical role in process industries, yet faces challenges from heterogeneous noise contamination and the coexistence of linear and nonlinear correlations in industrial datasets. These issues significantly compromise model prediction accuracy. To address this, we propose a fused correlation-based collaborative shared noise algorithm for robust soft-sensing modeling. The algorithm integrates Pearson correlation coefficients (linear relationships) and Spearman rank correlation coefficients (nonlinear relationships) to compute data credibility, thereby optimizing noise allocation under mixed correlation conditions. A convolutional neural network (CNN) is subsequently employed to construct the soft-sensing model. Experiments on a debutanizer column dataset demonstrate the superiority of the proposed method. The FC-CSNA outperforms baseline denoising techniques, including wavelet transform, denoising autoencoders, and the original collaborative shared noise algorithm, in noise suppression. The hybrid model achieves state-of-the-art prediction performance, with an R2score of 0.971 6 and mean squared error (MSE) of 0.001 1, validating its effectiveness in handling industrial data complexity. |
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