Visual process monitoring allows operators to identify and diagnose faults intuitively and quickly. The performance of visual process monitoring depends on the quality of extracted features and the performance of the visual model to visualize these features. In this study, we propose a deep model for feature extraction. First, a stacked auto-encoder is used to obtain the feature representation of the input data. Second, the feature representation is fed into a multi-layer perceptron (MLP) with the Fisher criterion as the objective function. The outputs of the MLP are the extracted discriminant features. We combine the proposed feature extraction method with the visual model consisting of t-distributed stochastic neighbor embedding (t-SNE) and a back-propagation (BP) neural network (t-SNE-BP) for visual process monitoring. In this method, an industrial data set is reorganized into a dynamic sample set containing fault trend. Subsequently, the proposed feature extraction method is used to extract discriminant features from the dynamic sample set. Finally, the t-SNE-BP model maps these discriminant features into a two-dimensional space in which the normal and fault states are represented by different regions. Visual process monitoring is performed on this two-dimensional space. The Tennessee Eastman process is used to demonstrate the performance of the proposed feature extraction and visual process monitoring methods.

可视过程监控使操作员可以直观，快速地识别和诊断故障。可视化过程监视的性能取决于提取特征的质量以及可视化这些特征的可视化模型的性能。在这项研究中，我们提出了一种用于特征提取的深度模型。首先，使用堆叠式自动编码器来获取输入数据的特征表示。其次，将特征表示以Fisher准则作为目标函数，输入到多层感知器（MLP）中。MLP的输出是提取的判别特征。我们将提出的特征提取方法与由t分布随机邻居嵌入（t-SNE）和反向传播（BP）神经网络（t-SNE-BP）组成的视觉模型进行视觉过程监控。用这种方法 将工业数据集重组为包含故障趋势的动态样本集。随后，提出的特征提取方法用于从动态样本集中提取判别特征。最后，t-SNE-BP模型将这些判别特征映射到二维空间中，在该二维空间中，正常状态和故障状态由不同的区域表示。在此二维空间上执行可视过程监视。田纳西州伊士曼过程用于演示所提出的特征提取和可视化过程监视方法的性能。t-SNE-BP模型将这些判别特征映射到二维空间，其中正常状态和断层状态由不同的区域表示。在此二维空间上执行可视过程监视。田纳西州伊士曼过程用于演示所提出的特征提取和可视化过程监视方法的性能。t-SNE-BP模型将这些判别特征映射到二维空间，其中正常状态和故障状态由不同区域表示。在此二维空间上执行可视过程监视。田纳西州伊士曼过程用于演示所提出的特征提取和可视化过程监视方法的性能。