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FRDPC subspace construction integrated with Bayesian inference for efficient monitoring of dynamic chemical processes
Modern chemical processes are usually characterized by large-scale,complex correlation,and strong dynamics,and monitoring of such processes is imperative.This paper proposes a performance-driven fault-relevant dynamic principal component(FRDPC) subspace construction integrated with Bayesian inference method to achieve efficient monitoring for dynamic chemical processes.First,dynamic principal component analysis is employed to deal with both auto-correlation and cross-correlation among variables.Second,considering fault information has no definite mapping to a certain dynamic principal component(DPC) and the existence of non-beneficial DPCs may cause redundancy in the monitoring,an FRDPC subspace is constructed for each fault through the performance-driven DPC selection.Then new process measurements are examined in each FRDPC subspace as well as the residual subspace.The monitoring results in all subspaces are fused to a comprehensive index through Bayesian inference to provide an intuitive indication of the process status.Case studies on a numerical example and the Tennessee Eastman benchmark process indicate the efficiency.
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第36届中国控制会议论文集(E)
2017年
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