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

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