Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares
Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares
复制标题
DOI：
10.1109/tii.2009.2033181
复制
发表时间：
2010-02
影响因子：
12.3
通讯作者：
Ying-wei Zhang;Hongxiao Zhou;S. Qin;T. Chai
中科院分区：
文献类型：
--
作者：
Ying-wei Zhang;Hongxiao Zhou;S. Qin;T. Chai
提出了一种基于多块核偏最小二乘（MBKPLS）的复杂过程分散故障诊断方法。为了解决非线性特征带来的问题，核偏最小二乘（KPLS）方法已经被提出。本文首次提出了MBKPLS算法，并将其应用于大规模过程的监控。MBKPLS的优点是：1）与偏最小二乘（PLS）相比，MBKPLS能够捕获块间和块内更多的有用信息; 2）与MBPLS相比，MBKPLS给出了非线性解释; 3）与KPLS相比，如果子块的数目等于变量的数目，则故障诊断成为可能。所提出的方法应用于连续退火过程的过程监测。应用结果表明，该分散监控方案有效地捕捉了过程中的复杂关系，极大地提高了诊断能力。
In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.