Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes

Convolutional neural network (CNN) models have been widely used for fault diagnosis of complex systems. However, traditional CNN models rely on small kernel filters to obtain local features from images. Thus, an excessively deep CNN is required to capture global features, which are critical for fault diagnosis of dynamical systems. In this work, we present an improved CNN that embeds global features (GF-CNN). Our method uses a multi-layer perceptron (MLP) for dimension reduction to directly extract global features and integrate them into the CNN. The advantage of this method is that both local and global patterns in images can be captured by a simple model architecture instead of establishing deep CNN models. The proposed method is applied to the fault diagnosis of the Tennessee Eastman process. Simulation results show that the GF-CNN can significantly improve the fault diagnosis performance compared to traditional CNN. The proposed method can also be applied to other areas such as computer vision and image processing.