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- Title
Output-relevant fault detection and identification of chemical process based on hybrid kernel T-PLS.
- Authors
Zhao, Xiaoqiang; Xue, Yongfei
- Abstract
The single kernel total projection to latent structures (T-PLS) would lead to a higher missing alarm rate and false alarm rate because either global kernel function or local kernel function can be utilized. The hybrid kernel T-PLS algorithm proposed in this paper combines global function with local function to solve non-linear problems by projecting low-dimensional input data to high-dimensional feature space. The feature space is then divided into output directly correlated subspace, output orthogonal subspace, output uncorrelated subspace, and residual subspace by output variables. Based on fault detected by statistic D and Q in these subspaces, respectively, fault-free data are reconstructed and the fault magnitude is estimated by generalized reconstruction-based contribution (RBC). The simulation results of Tennessee Eastman process show the proposed algorithm can not only detect output-relevant fault with higher detection rate, but also identify the type of fault.
- Subjects
KERNEL functions; KERNEL (Mathematics); LATENT structure analysis; ALGORITHMS; CHEMICAL processes
- Publication
Canadian Journal of Chemical Engineering, 2014, Vol 92, Issue 10, p1822
- ISSN
0008-4034
- Publication type
Article
- DOI
10.1002/cjce.22031