Fault Detection Of Batch Process Based On Optimized Adaptive DTW By AFSA-AP Clustering

With the increasing complexity of batch production process,people put forward higher requirements for the safety of production process.Data-driven method is an effective method for fault detection in batch process.In recent years,Principal Component Analysis(PCA)has been widely used in batch processes.Its derivative method,Multidirectional Kernel Principal Component Analysis(MKPCA),has become the main force in the field of fault detection.The traditional statistical analysis method is based on the assumption that the length of each batch is the same.In actual production,this assumption is not valid due to many factors such as machine differences.Based on process data and considering the main characteristics of batch process,this paper proposes two fault detection methods for batch process based on dynamic time warping(DTW)and affine propagation clustering(AP clustering).Firstly,aiming at the unequal length of batch data in batch process,an adaptive DTW algorithm is proposed,which combines MKPCA method to detect batch process faults.The traditional DTW method can easily cause a lot of loss of data information when processing batch data.The adaptive DTW algorithm combines the symmetric algorithm with the asymmetric algorithm flexibly.The batch trajectory synchronization can avoid data waste and shorten the time needed for synchronization.Secondly,in order to improve the accuracy of weight matrix calculation in adaptive DTW algorithm,a method based on Artificial Fish-swarm algorithm(AFSA)is proposed,that is,AFSA-AP clustering weight matrix optimization design method.This method uses AFSA to optimize the parameters in AP clustering,then uses AP clustering algorithm to filter process data,and chooses the most important part of process data for off-line modeling to improve the accuracy of weight matrix.Finally,it improves the MKPCA fault detection effect based on adaptive DTW algorithm.Taking penicillin fermentation process fault detection as an example,the validity of weight matrix optimization design method based on AFSA-AP clustering was verified.Finally,the fault detection results are visualized in the GUI interface,and the interaction between the staff and the interface is established to facilitate the operation of the detection process.The weight calculation module based on AFSA-AP clustering algorithm,time-length synchronization module based on adaptive DTW and MKPCA fault detection module are designed.Through the software development of the fault detection algorithm integration,users can choose the appropriate weight calculation method and time length synchronization method to realize the visualization of various fault detection algorithms.