Fault detection with principal component pursuit method

Pan, Yijun; Yang, Chunjie; Sun, Youxian; An, Ruqiao; Wang, Lin

doi:10.1088/1742-6596/659/1/012035

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Calculate the online Hotelling’s T 2 statistic by where d i is the i th row of normalized testing matrix D …”

Section: Fault Detection With Minor Faults In the Training Matrixmentioning

confidence: 99%

“…In 2011, the PCP method was used for process monitoring for the first time by Isom et al; they illustrated that PCP is robust to outliers and that it can detect and isolate faults simultaneously by observing the obtained sparse matrix. A new standardized method and a residual generator suitable for PCP generated process models were developed by Cheng et al Pan et al proposed a new mean-correlation statistic suitable for online process monitoring based on the PCP method . A coordinate descent algorithm based on PCP and its convergence proof that directly utilized a Lyapunov approach were also presented by Cheng et al A process monitoring model and an online monitoring statistic with stable PCP were developed by Yan et al Pan et al introduced a novel IPCP method derived from low rank representation (LRR) and PCP methods, and an online process monitoring statistic was also developed …”

Section: Introductionmentioning

confidence: 99%

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Robust Principal Component Pursuit for Fault Detection in a Blast Furnace Process

Pan

Yang

et al. 2017

Ind. Eng. Chem. Res.

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Since blast furnaces are generally controlled by operators, the minor faults regarded as disturbances might be contained in the collected data matrix. This can severely affect sample distributions, which leads to arbitrary fault detection results using traditional data-driven methods. In this paper, a novel fault detection method named robust principal component pursuit (PCP) to handle minor faults is proposed. The minor faults are separated from columns and rows, respectively, in the training matrix via two matrix norms. By applying the proposed robust PCP method, a low rank matrix containing important process information, as well as explicit variable relationships, and a block sparse matrix containing minor faults are derived. Moreover, the convergence of the proposed method is discussed. Hotelling’s T 2 statistic is potentially useful for online process monitoring in the low rank matrix. Finally, to evaluate the decomposition capacity of the proposed method for a matrix containing minor faults, a comparison of the proposed method with other robust methods is presented. To test the effectiveness of the proposed method for fault detection, a numerical simulation is adopted at first. Finally, the power of the proposed method is illustrated in a real blast furnace process.

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“…Calculate the online Hotelling’s T 2 statistic by where d i is the i th row of normalized testing matrix D …”

Section: Fault Detection With Minor Faults In the Training Matrixmentioning

confidence: 99%