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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process
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A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process

机译：用于深度学习网络的层面数据增强策略及其在工业加氢裂化过程中的软传感器应用

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摘要

In industrial processes, inferential sensors have been extensively applied for prediction of quality variables that are difficult to measure online directly by hard sensors. Deep learning is a recently developed technique for feature representation of complex data, which has great potentials in soft sensor modeling. However, it often needs a large number of representative data to train and obtain a good deep network. Moreover, layer-wise pretraining often causes information loss and generalization degradation of high hidden layers. This greatly limits the implementation and application of deep learning networks in industrial processes. In this article, a layer-wise data augmentation (LWDA) strategy is proposed for the pretraining of deep learning networks and soft sensor modeling. In particular, the LWDA-based stacked autoencoder (LWDA-SAE) is developed in detail. Finally, the proposed LWDA-SAE model is applied to predict the 10% and 50% boiling points of the aviation kerosene in an industrial hydrocracking process. The results show that the LWDA-SAE-based soft sensor is superior to multilayer perceptron, traditional SAE, and the SAE with data augmentation only for its input layer (IDA-SAE). Moreover, LWDA-SAE can converge at a faster speed with a lower learning error than the other methods.
机译：在工业过程中，推理传感器已广泛应用于预测难以通过硬传感器直接衡量的质量变量。深度学习是最近开发的复杂数据特征表示的技术，其在软传感器建模中具有很大的潜力。但是，它通常需要大量的代表数据来训练和获得一个很好的深网络。此外，层明显预先训练通常会导致高隐藏层的信息损失和泛化劣化。这极大地限制了深度学习网络在工业过程中的实施和应用。在本文中，提出了一个层次的数据增强（LWDA）策略，用于深入学习网络和软传感器建模的预先预测。特别地，详细开发了基于LWDA的堆叠的AutoEncoder（LWDA-SAE）。最后，拟议的LWDA-SAE模型用于预测工业加氢裂化过程中航空煤油的10％和50％沸点。结果表明，基于LWDA-SAE的软传感器优于多层的感知，传统的SAE，以及仅针对其输入层（IDA-SAE）的数据增强的SAE。此外，LWDA-SAE可以以比其他方法的较低的学习误差更快地收敛。

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