Premium
Output‐relevant fault detection and identification of chemical process based on hybrid kernel T‐PLS
Author(s) -
Zhao Xiaoqiang,
Xue Yongfei
Publication year - 2014
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22031
Subject(s) - kernel (algebra) , subspace topology , linear subspace , fault detection and isolation , pattern recognition (psychology) , algorithm , projection (relational algebra) , fault (geology) , statistic , mathematics , kernel method , false alarm , constant false alarm rate , computer science , artificial intelligence , statistics , support vector machine , geometry , combinatorics , seismology , actuator , geology
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.