A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification
A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification
复制标题
用于工业过热度识别的半监督拉普拉斯极限学习机以及与 CNN 的特征融合
DOI：
10.1016/j.neucom.2019.11.012
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发表时间：
2020-03-14
期刊：
影响因子：
6
通讯作者：
Xie, Yongfang
中科院分区：
文献类型：
--
作者：
Lei, Yongxiang;Chen, Xiaofang;Xie, Yongfang
工业铝电解槽的电解度是维持电解槽能量平衡、提高电流效率、提高产量的关键指标。然而，现有的SD识别主要依靠人工经验，SD的准确性远远不能令人满意。此外，人工成本和物理设备昂贵且耗时。在本文中，我们提出了一种深软测量方法SD检测。首先，利用CNN进行火焰孔图像特征提取。其次，集成拉普拉斯正则化的半监督极端学习机（ELM）进一步用于SD分类。本文的主要贡献在于：（1）提出的CNN-LapsELM算法利用CNN进行火焰孔图像特征提取，然后利用ELM进行进一步分类，充分利用了CNN的复杂特征提取能力、ELM良好的泛化能力和较高的计算效率。(2)标记和未标记的样本都用于CNN-LapsELM训练过程。它充分利用了未标记数据中包含的信息。同时，利用拉普拉斯正则化学习孔洞图像样本的流形结构，从而提高了CNN-LapsELM的性能。(3)提出的CNN-LapsELM算法提高了泛化能力和鲁棒性。对比结果表明，CNN-LapsELM的识别精度优于现有的SD识别方法，其准确率为87%，优于现有的SD识别方法上级。(C)2019由Elsevier B. V.出版
The superheat degree (SD) in industrial aluminum electrolysis cell is a critical index that can maintain the energy balance, improve the current efficiency and improve production. However, the existing SD identification is mainly relying on artificial experience and the accuracy of SD is far from satisfactory. Further, artificial costs and physical equipment are expensive and time-consuming. In this paper, we propose a deep soft sensor method for SD detection. First, CNN is utilized for flame hole image feature extraction. Second, a semi-supervised extreme learning machine (ELM) that integrates Laplacian regularization is further used for SD classification. The main contributions of the paper are: (1) The proposed CNN-LapsELM utilizes the CNN for flame hole image feature extraction and then ELM for further classification, which fully takes advantage of CNN's ability for complex feature extraction, ELM's excellent generalization ability, and high computation efficiency. (2) Both the labeled and unlabeled samples are utilized for the CNN-LapsELM training process. It fully leverages the information contained in unlabeled data. At the same time, Laplacian regularization is utilized for learning the manifold structure of hole image samples, so the performance of the proposed CNN-LapsELM are improved. (3) The proposed CNN-LapsELM algorithm improves the generalization ability and robustness. The comparison result demonstrates that the CNN-LapsELM is superior to the existing SD identification and the accuracy is 87%. (C) 2019 Published by Elsevier B.V.