Industrial process fault detection based on IMDS-DLNS method - Liwei FENG; Liwen SUN; Huan GU; Yuan LI (Journal of Hebei University of Science and Technology, 2022)
Aiming at the problem that when the multidimensional scaling (MDS) method is used to reduce the dimensionality of high-dimensional data,the new sample lacks the mapping matrix and cannot carry out low-dimensional embedding,an incremental multidimensional scaling (IMDS) method was proposed.Firstly,the dual local nearest neighbor standardization (DLNS) technology was introduced to solve the problem of data having multiple centers and obvious variance differences after IMDS dimensionality reduction.Secondly,Hotelling T2 statistics was used to monitor the process,and a fault detection method (IMDS-DLNS) with incremental multi-dimensional scale transformation and double local neighbor standardization was constructed.Finally,through numerical simulation of the process and penicillin fermentation process,the IMDS-DLNS method is compared with PCA,KPCA,FD-KNN and other methods,respectively.The results show that IMDS-DLNS has a higher fault detection rate compared to other methods.IMDS-DLNS method has good fault detection capabilities for multivariable and multimodal processes,and can guarantee product quality and production safety,which provides some reference for industrial process fault detection.