Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique i...Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).展开更多
Fault prediction for a class of unknown-model multivariate continuous processes with a hidden fault was studied,and a solution was given based on statistical process monitoring(SPM)approach.A principle component analy...Fault prediction for a class of unknown-model multivariate continuous processes with a hidden fault was studied,and a solution was given based on statistical process monitoring(SPM)approach.A principle component analysis(PCA)model using sample data under normal state was built,then the characteristic value for fault prediction was constructed,and time series analysis and prediction were applied to the characteristic value to predict the remaining useful life(RUL)of the system.Aiming at the linear time invariant system,a characteristic value was proposed and the prediction error of RUL was analyzed under some assumptions for system structure and hidden fault.A case study on a CSTR showed the efficiency of the proposed approach.展开更多
基金Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry.
文摘Constrained spectral non-negative matrix factorization(NMF)analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops,and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis(ICA).
文摘Fault prediction for a class of unknown-model multivariate continuous processes with a hidden fault was studied,and a solution was given based on statistical process monitoring(SPM)approach.A principle component analysis(PCA)model using sample data under normal state was built,then the characteristic value for fault prediction was constructed,and time series analysis and prediction were applied to the characteristic value to predict the remaining useful life(RUL)of the system.Aiming at the linear time invariant system,a characteristic value was proposed and the prediction error of RUL was analyzed under some assumptions for system structure and hidden fault.A case study on a CSTR showed the efficiency of the proposed approach.
