基于批次图像化的卷积自编码故障监测方法

针对间歇过程的非线性、多阶段性等特点及其三维数据形式,提出基于批次图像化的卷积自编码故障监测方法.首先,将每个批次数据看作一个灰度图,每个批次中数据变化可以看作图片的纹理变化,利用卷积自编码器(convolutional autoencoder,CAE)直接对间歇过程三维数据进行特征提取,避免三维数据展开成二维时导致的信息丢失,无需分阶段充分考虑批次全局信息,有效提取过程变量相关关系的动态变化;同时,利用卷积操作提取局部特征信息,自编码网络可以解决非线性问题,实现特征的无监督学习;然后,使用一类支持向量机(one-class support vector method, OCSVM)描述特征分布,构造新的统计量,确定控制限,实现故障监测;最后,通过将该方法应用到Pensim仿真平台及重组人粒细胞集落刺激因子发酵的实际生产数据,验证所提方法的准确性和有效性.

Fault detection of batch image-based convolutional autoencoder

Aiming at nonlinearity, multi phases and 3D data matrixes in batch processes, a fault detection method using a batch image-based convolutional autoencoder is proposed. Process data of each batch is considered as a grayscale image and is input to the convolutional autoencoder(CAE) directly for representation learning. Data variation in each batch can be regarded as the texture change of the image. Information loss caused by 3D data unfolding to 2D is avoided. Meanwhile, variable correlation is effectively extracted using global modeling with no need to phase division. Convolution operation extracts local conjunction features, and using a autoencoder is an efficient way for unsupervised learning. Then the one-class support vector methold(OCSVM) is used to constructe monitoring statistic and calculate control limit for fault detection. By applying the proposed method on the Pensim simulation and recombinant human granulocyte colony-stimulating factor (rhG-CSF) fermentation process, the effectiveness is demonstrated.