High repeatability of similar information but a lack of typical fault features, in the monitoring data of distillation processes for continuous production, leads to a small proportion of data with labels. Therefore, the requirement for a large number of labeled samples in conventional deep learning models cannot be met, resulting in significant performance degradation in their anomaly identification. In this paper, an intelligent anomaly identification method for small samples is proposed, based on semisupervised deep learning. Specifically, on the basis of a deep denoising autoencoder (DAE), semisupervised ladder networks (SSLN) is constructed to use a large number of unlabeled, process data to assist the supervised learning process, thus improving the performance of the anomaly identification model. In order to construct the optimum SSLN model, the influences of parameters such as the number of deep network layers, the proportion of labeled samples, and the noise intensity on identification accuracy are analyzed while making the information flow in the network more efficient. Experimental results of anomaly identification in the depropanization distillation process show that compared with the conventional multilayer perception (MLP) and convolutional neural network (CNN)-DAE models, the proposed method can obtain a higher diagnostic accuracy in the case with limited labeled process data.

中文翻译：

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基于半监督阶梯网络的精馏过程中带有少量标记数据的异常识别

在连续生产的蒸馏过程的监控数据中，相似信息的重复性很高，但缺乏典型的故障特征，导致带有标签的数据比例很小。因此，无法满足常规深度学习模型中对大量标记样本的需求，从而导致其异常识别中的显着性能下降。本文提出了一种基于半监督深度学习的小样本智能异常识别方法。具体来说，在深度降噪自动编码器（DAE）的基础上，半监督梯形网络（SSLN）被构造为使用大量未标记的过程数据来辅助监督学习过程，从而提高了异常识别模型的性能。为了构建最佳的SSLN模型，分析了深层网络的数量，标记样本的比例和噪声强度等参数对识别精度的影响，同时使网络中的信息流更加高效。在脱丙烷蒸馏过程中进行异常识别的实验结果表明，与常规的多层感知（MLP）和卷积神经网络（CNN）-DAE模型相比，该方法在标记过程数据有限的情况下可以获得更高的诊断准确性。