Fault prediction of multivariate batch process based on multi-sampled sequence feature extraction network
Fault prediction can indicate abnormal changes in variables and predict fault conditions in advance.Existing fault prediction methods primarily consider the global temporal dependencies of the complete sequence,which neglecting the dependencies between variables and the distinct local temporal features in the sampled subsequences.To address the above issues,a fault prediction architecture based on multi-sampled sequence feature extraction network(MSFEN)is proposed.First,a batch joint embedding mechanism is designed to better express the dependencies between variables while considering batch periodicity.Then,a sequence sampling mechanism is developed to divide the complete time series into sampled subsequences of different scales.Subsequently,the invert smoothing Transformer and the convolutional interactive extraction module are designed to comprehensively extract multi-scale temporal dependencies and variable dependencies.Finally,the multi-sampled sequence features are fused to obtain the final encoding features,and fault prediction is achieved through the feed forward layer.Experiments are conducted using the penicillin fermentation process,and the results show that this method has good fault prediction performance.
万方数据
维普
