Fault diagnosis of TE process based on incremental learning

With the continuous research of deep neural networks, the network structure becomes more and more flexible, and it has achieved good results in terms of recognition, classification and so on. Traditional deep neural networks use one-time batch learning and suffer catastrophic forgetting when learning new data. This paper proposes a new fault diagnosis algorithm based on incremental structure. Through the use of improved residual neural networks, enhanced training sets, and enhanced loss functions, new knowledge can be continuously learned from new data while retaining what has been learned, thereby slowing catastrophic forgetting. Experiment demonstrates the effectiveness of the algorithm for mitigating catastrophic forgetting by using the Tennessee-Eastman (TE) chemical process dataset.