A Distributed Monitoring Method for Ternary Cathode Materials Sintering Process Based on Gaussianity Preserved DNN-Aided CCA

The sintering process is crucial for the preparation of ternary cathode materials (TCMs), which need to be precisely monitored to ensure the production of high-quality products. Nevertheless, the sintering process of ternary cathode materials (TCMs-SP) is marked by a prolonged duration, the high dimensionality of process variables, and spatio-temporal correlation coupling, which poses challenges when using traditional centralized monitoring methods. To this end, a novel distributed monitoring method suitable for TCMs-SP is proposed in this article. First, the sintering process is divided into different subsystems based on the material manufacturing process. Then, during the establishment of the local monitoring model, using the low-dimensional strong correlation information transmitted by other subsystems, the Gaussianity preserved deep neural network-aided canonical correlation analysis (CCA) is utilized to extract the Gaussianized nonlinear dynamic features. On this basis, the monitoring statistics of the input and output observation space are developed via CCA, and a monitoring strategy is specially designed for TCMs-SP. Finally, the industrial application verifies that our proposed approach offers superior monitoring compared to existing methods. It allows for rapid localization of faults and enhances troubleshooting efficiency in the sintering process.