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Fault Detection for Time-Varying Processes
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DOI：10.1109/TCST.2013.2273498.png)
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摘要
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In this brief, a new manifold learning method is proposed. Then, a process monitoring approach is proposed for handling the multimode monitoring problem in the electro-fused magnesia furnace based on the proposed manifold learning method. In the conventional methods, only partial common information is shared by different modes, i.e., the common eigenvectors. Compared with the conventional methods, the contributions are a new method of extracting the common subspace of different modes is proposed based on the manifold learning. The common subspace extracted by the proposed manifold learning method is shared by all different modes, and after those two different subspaces are separated, the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the manifold subspaces.
Keyword:
Common subspace
electro-fused magnesia furnace (EFMF)
fault detection
nonlinear multimode process monitoring
specific subspace
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