Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new large-scale process monitoring method based on knowledge mining is proposed. The contributions are as follows: 1) beten-mode independent similarities are explored to reveal the beten-mode relationship for multimode model development and online monitoring; 2) comprehensive subspace decomposition is performed in each mode regarding their relative similarities and influences on process monitoring; 3) each mode is separated into three different systematic subspaces and one residual subspace (RS) based on the beten-mode similarities; and 4) different variations are modeled, respectively, for online monitoring to identify mode affiliation and detect the fault status. The proposed method is applied to fault detection of Tennessee Eastman process (TE Process). The monitoring results show the effectiveness of the proposed method, compared to the conventional monitoring method.
['Yingwei Zhang', 'Yunpeng Fan', 'Nan Yang']
I am writing a research paper on a idea Fault Diagnosis of Multimode Processes Based on Similarities
[{"from": "human", "value": "I am writing a research paper on a idea Fault Diagnosis of Multimode Processes Based on Similarities"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new large-scale process monitoring method based on knowledge mining is proposed. The contributions are as follows: 1)\u00a0beten-mode independent similarities are explored to reveal the beten-mode relationship for multimode model development and online monitoring; 2)\u00a0comprehensive subspace decomposition is performed in each mode regarding their relative similarities and influences on process monitoring; 3)\u00a0each mode is separated into three different systematic subspaces and one residual subspace (RS) based on the beten-mode similarities; and 4)\u00a0different variations are modeled, respectively, for online monitoring to identify mode affiliation and detect the fault status. The proposed method is applied to fault detection of Tennessee Eastman process (TE Process). The monitoring results show the effectiveness of the proposed method, compared to the conventional monitoring method."}]