  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.
