Research on the Application of Chemical Process Fault Diagnosis Methods Based on Neural Network

With the continuous development and progress of the industrial generation level, the relationship between the nodes in the chemical process, manufacturing process and other industrial processes is becoming more and more complex, and the chances of failures are increasing, people are paying more and more attention to the safe production and reliable and smooth operation of the chemical process. To address this issue, fault detection and diagnosis are performed by different neural network algorithms on different Tennessee Eastman chemical process datasets, and the prediction accuracy of these algorithms on different datasets is compared.