Online incipient fault diagnosis based on Kullback‐Leibler divergence and recursive principle component analysis

Chai, Yi; Tao, Songbing; Mao, Wanbiao; Zhang, Ke; Zhu, Zhiqin

doi:10.1002/cjce.23137

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“…In addition, from the view of probability density function (PDF), the Kullback Leibler divergence-PCA method was implemented by Harmouche et al 17 because of its high sensitivity for incipient faults. Chen et al 18 and Chai et al 19 also discussed the combination of KLD and PCA for incipient fault detection.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Deng

2019

Ind. Eng. Chem. Res.

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Early detection of incipient faults is a challenging task in chemical process monitoring field. As an effective incipient fault detection tool, statistical local kernel principal component analysis (SLKPCA) has demonstrated its advantage over the traditional kernel principal component analysis (KPCA). However, how to improve its incipient fault detection performance is still a valuable problem. In this paper, an enhanced SLKPCA method, referred to as two-dimensional weighted SLKPCA (TWSLKPCA), is proposed by integrating the sample and component weighting strategies. Different to KPCA, SLKPCA monitors the process changes based on the residual vectors computed by the statistical local approach. To highlight the influence of the faulty residual samples, the residual sample weighting strategy is first designed based on the distance between the tested samples and the training samples, which puts large weights on the samples with strong fault information. Furthermore, the residual component weighting strategy is developed to assign large weights to the sensitive components, which are judged by computing the mutual information between the sampleweighted residual components and the original measured variables. Based on the two-dimensional weighted residual vectors, two monitoring statistics are built to detect the incipient faults. Finally, simulations on a numerical example and the Tennessee Eastman process are used to demonstrate the superiority of the proposed TWSLKPCA method.

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Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Deng

2019

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

Online incipient fault diagnosis based on Kullback‐Leibler divergence and recursive principle component analysis

Cited by 1 publication

References 1 publication

Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

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