Process fault detection, isolation, and reconstruction by principal component pursuit

Isom, Joshua D.; LaBarre, Robert E.

doi:10.1109/acc.2011.5990849

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Under certain conditions, and with a Lagrange multiplier , optimization problem (9) recovers a low-rank matrix corresponding to a fault-free process condition and a sparse matrix that has nonzero entities corresponding to sensor and process faults, which can be considered as sensor reading abnormalities or sharp changes in the system (cf. [36]). The matrix is the selected flow sensor reading data vector̃in our case.…”

Section: Outlier Eliminationmentioning

confidence: 99%

Systematic Prioritization of Sensor Improvements in an Industrial Gas Supply Network

Babat

Esmaili

Isom

et al. 2017

International Journal of Chemical Engineering

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The paper analyzes the impact of the sensor reading errors on parameters that affect the production costs of a leading US industrial gas supply company. For this purpose, a systematic methodology is applied first to determine the relationship between the system output and input parameters and second to identify the assigned input sensors whose readings need to be improved in a prioritized manner based on the strength of those input-output relationships. The two main criteria used to prioritize these sensors are the decrease in production costs and the decrease in production costs’ volatility obtained when the selected sensor’s precision is improved. To illustrate the effectiveness of the proposed approach, we first apply it to a simplified version of the real supply network model where the results can be readily validated with the simulated data. Next, we apply and test the proposed approach in the real supply network model with historical data.

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Section: Outlier Eliminationmentioning

confidence: 99%

Systematic Prioritization of Sensor Improvements in an Industrial Gas Supply Network

Babat

Esmaili

Isom

et al. 2017

International Journal of Chemical Engineering

Self Cite

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“…Process monitoring is such an area. However, to our best knowledge there has been only one paper on this topic [13]. In [13], a comparison between the process monitoring approaches based on PCA and PCP was given.…”

Section: Introductionmentioning

confidence: 98%

“…However, to our best knowledge there has been only one paper on this topic [13]. In [13], a comparison between the process monitoring approaches based on PCA and PCP was given. A conclusion was drawn that the PCP technique was promising in process monitoring because the PCP-based method could overcome most of the shortcomings of PCAbased methods.…”

Section: Introductionmentioning

confidence: 98%

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Application of principal component pursuit to process fault detection and diagnosis

Cheng

Chen

2013

2013 American Control Conference

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Data-driven process monitoring has been extensively discussed in both academia and industry because of its applicability and effectiveness. One of the most applied techniques is the principal component analysis (PCA). Recently a new technique called principal component pursuit (PCP) is introduced. Compared to PCA, PCP is more robust to outliers. In this paper, the application of the PCP technique to process monitoring is thoroughly discussed from training data preprocessing to residual signal post-filtering. A new scaling preprocessing step is proposed to improve quality of data matrices in the sense of low coherence. A residual generator and a post-filter suitable for PCP generated process models are also provided. The post-filtered residual represents the fault signal, which makes the fault detection, isolation and reconstruction procedures simple and straightforward. A numerical example is provided to describe and illustrate the PCP-based process modeling and monitoring procedures.

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“…Candes et al introduced a state-of-the-art robust PCA method named principal component pursuit (PCP) that decomposes a data matrix into a low rank part and a sparse part . 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 .…”

Section: Introductionmentioning

confidence: 99%

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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Process fault detection, isolation, and reconstruction by principal component pursuit

Cited by 8 publications

References 7 publications

Systematic Prioritization of Sensor Improvements in an Industrial Gas Supply Network

Systematic Prioritization of Sensor Improvements in an Industrial Gas Supply Network

Application of principal component pursuit to process fault detection and diagnosis

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

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