Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS
Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS
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DOI：
10.1016/j.ces.2010.10.008
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发表时间：
2011
影响因子：
4.7
通讯作者：
Ying-wei Zhang;Chi Ma
中科院分区：
文献类型：
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作者：
Ying-wei Zhang;Chi Ma
提出了一种新的基于不同尺度的核主成分分析(KPCA)和核偏最小二乘(KPLS)模型的非线性过程监测与故障诊断方法，称为多尺度KPCA(MSKPCA)和多尺度KPLS(MSKPLS)。对这些多尺度数据应用核主成分分析和核偏最小二乘法来捕捉不同尺度上的过程变量相关性。本文的主要贡献是提出了基于多尺度贡献图的非线性故障诊断方法。特别地，导出了各尺度上变量的非线性分数。这些非线性尺度的贡献是可以计算的，这对于诊断主要发生在单个尺度上的故障非常有用。将所提出的方法应用于连续退火炉和电熔镁炉的过程监控。应用结果表明，该方法有效地捕捉了过程中的复杂关系，提高了诊断能力。
New approaches are proposed for nonlinear process monitoring and fault diagnosis based on kernel principal component analysis (KPCA) and kernel partial least analysis (KPLS) models at different scales, which are called multiscale KPCA (MSKPCA) and multiscale KPLS (MSKPLS). KPCA and KPLS are applied to these multiscale data to capture process variable correlations occurring at different scales. Main contribution of the paper is to propose nonlinear fault diagnosis methods based on multiscale contribution plots. In particular, the nonlinear scores of the variables at each scale are derived. These nonlinear scale contributions can be computed, which is very useful in diagnosing faults that occur mainly at a single scale. The proposed methods are applied to process monitoring of a continuous annealing process and fused magnesium furnace. Application results indicate that the proposed approach effectively captures the complex relations in the process and improves the diagnosis ability.