Dynamic process monitoring based on WD-MNPE-CVA for hot strip mill plant-wide process
Process monitoring technology is an effective measure to ensure the safety and efficiency of the hot strip mill plant-wide process.Considering the"nonlinear,multimode,dynamic"characteristics of the hot strip mill process,a weighted difference-modified neighborhood preserving embedding-canonical variable analysis(WD-MNPE-CVA)method for dynamic process monitoring of plant-wide process is proposed in this paper.Firstly,in view of the nonlinear and mul-timode characteristics existing in the process data,the weighted difference method is used to preprocess the process data.Then the plant-wide process is divided based on the mechanism knowledge.An improved neighborhood preserving embed-ding algorithm is developed to obtain more accurate neighborhood relationship of each sample point based on Euclidean distance and cosine distance between sample points,and then establish local dynamic monitoring model of each subpro-cess based on canonical variable analysis.Finally,a global dynamic process monitoring model is established by Bayesian inference,and the effectiveness of the proposed method is verified by the actual fault data of hot strip mill process.
process monitoringneighborhood preserving embeddingcanonical variable analysisBayesian fusionhot strip mill plant-wide process
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