Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers...Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.展开更多
D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two ...D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two correlated variables.This leads to increased variance in contribution estimation and hence poor separability of faulty and normal variables.A new method for contribution calculation to D-statistic is proposed here which introduces a weighting scheme capable of distinguishing the contributions of two correlated variables.Simulation examples show that the proposed approach achieves improved resolution for distinguishing faulty and normal conditions.展开更多
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(12CX06071A)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.
基金the National Basic Research Program (973) of China(No.2010CB731800)the National Natural Science Foundation of China(Nos.60974059, 60736026 and 61021063)
文摘D-statistic contribution analysis has been frequently used in practice for fault diagnosis.Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two correlated variables.This leads to increased variance in contribution estimation and hence poor separability of faulty and normal variables.A new method for contribution calculation to D-statistic is proposed here which introduces a weighting scheme capable of distinguishing the contributions of two correlated variables.Simulation examples show that the proposed approach achieves improved resolution for distinguishing faulty and normal conditions.
