Deep learning has been successfully applied in process monitoring in recent years due to its powerful feature extraction. However, these monitoring methods are difficult to extract intrinsic representations of the process data in complex nonlinear processes. A new deep neural network, residual attention convolutional autoencoder (RACAE) is proposed for process monitoring. The unsupervised learning method of RACAE can extract representative features from high-dimensional data, which can significantly improve process monitoring performance in nonlinear processes. RACAE effectively integrates convolution calculation with an autoencoder to perform effective feature extraction of multivariate data. Moreover, residual attention block is embedded in the autoencoder to select these key features and then reduce the feature dimension for detector. A new process monitoring model is proposed and two kinds of statistics are developed for fault detection. The effectiveness of RACAE in fault detection is evaluated through a numerical case and two benchmark processes. The convolutional autoencoder based on residual attention provides a new approach for feature learning and process monitoring of complex processes.

由于深度学习功能强大的特征提取，近年来已成功地将其应用于过程监控中。但是，这些监视方法很难提取复杂非线性过程中过程数据的内在表示。提出了一种新的深度神经网络，即剩余注意力卷积自动编码器（RACAE），用于过程监控。RACAE的无监督学习方法可以从高维数据中提取代表性特征，从而可以显着提高非线性过程中的过程监视性能。RACAE有效地将卷积计算与自动编码器集成在一起，以对多变量数据进行有效的特征提取。此外，将剩余的注意力块嵌入到自动编码器中以选择这些关键特征，然后减小检测器的特征尺寸。提出了一种新的过程监控模型，并开发了两种统计数据进行故障检测。通过数值案例和两个基准过程评估了RACAE在故障检测中的有效性。基于剩余注意力的卷积自动编码器为复杂过程的特征学习和过程监视提供了一种新方法。