Fault Diagnosis of Chemical Production System Based on Improved KFDA

With the rapid development of chemical industry, the traditional KFDA has a series of problems, such as high computational complexity, poor real-time performance and limited ability to process highly nonlinear chemical production data. In view of the above problems, this study proposes an improved KFDA algorithm to solve the above problems, improve the accuracy of fault diagnosis of chemical production system, reduce the false alarm rate, enhance real-time performance and reduce the missed detection rate. The improved KFDA adopts advanced feature extraction technology, which optimizes the adjustment of parameters and reduces the spatial complexity of the algorithm. In fault diagnosis, data preprocessing, feature extraction, improved KFDA and fault detection and classification in improved KFDA are carefully designed. From the calculation results, the minimum fault identification accuracy of the improved KFDA algorithm reaches 80.5%, and the false alarm rate and missed detection rate are relatively low, which effectively supports the stable operation and safe production of the chemical production system.