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| 科研成果 |
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| 论文题目： |
Correlated and weakly correlated fault detection based on variable division and ICA |
| 第一作者： |
Li S(李帅);Zhou XF(周晓锋);Pan FC(潘福成);Shi HB(史海波);Li KT(李开拓);Wang ZW(王中伟) |
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| 发表刊物： |
Computers and Industrial Engineering |
| 发表年度： |
2017 |
| 卷,期,页： |
112,,320-335 |
| 论文出处： |
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| 论文摘要： |
In many industrial processes, the correlations of multiple variables are complicated. Some variables are correlated and some are weakly correlated with others, which should be considered in process modelling and fault detection. This paper proposes a correlated and weakly correlated fault detection approach, which is mainly based on variable division and independent component analysis (ICA). A few variables are weakly correlated with others and fault detection should be implemented separately for correlated and weakly correlated subspaces. Firstly, variable division based on weighted proximity measure is presented to obtain correlated and weakly correlated variables. Then, ICA is used for fault detection in correlated subspace and weakly correlated subspace, which needs not kernel mapping or kernel parameter setting. Finally, comprehensive statistics are built based on different subspaces. The proposed method considers the correlated and weakly correlated characteristics of variables and the advantages of ICA in handling weakly correlated variables. The monitoring results of the numerical system and Tennessee Eastman (TE) process have been used to demonstrate effectiveness and superiority of the proposed approach. |
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