TY - JOUR
T1 - Detecting special-cause variation ‘events’ from process data signatures
AU - Young, T.M.
AU - Khaliukova, O.
AU - André, N.
AU - Petutschnigg, A.
AU - Rials, T.G.
AU - Chen, C.-H.
N1 - Cited By :1
Export Date: 14 December 2023
Correspondence Address: Young, T.M.; Center for Renewable Carbon, 2506 Jacob Drive, United States; email: [email protected]
Funding details: U.S. Department of Energy, USDOE, R11-3215-096
Funding details: Cooperative State Research, Education, and Extension Service, CSREES, TEN00MS-107
Funding text 1: This work was supported by Cooperative State Research, Education, and Extension Service [grant number TEN00MS-107, Young]; U.S. Department of Energy [grant number R11-3215-096].
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PY - 2019/6/11
Y1 - 2019/6/11
N2 - The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using the control band method for data signatures from near-infrared (NIR) spectroscopy scans of industrial fibers of Switchgrass (Panicum virgatum) used for biofuels production, Loblolly Pine (Pinus taeda) fibers for medium density fiberboard production, and formaldehyde (HCHO) emissions from particleboard were used. Simulations curves (k) of k = 100, k = 1000, and k = 10,000 indicate that the Bonferroni method for detecting special-cause variation is closely aligned with the Shewhart definition of control limits when the pdfs are Gaussian or lognormal.
AB - The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using the control band method for data signatures from near-infrared (NIR) spectroscopy scans of industrial fibers of Switchgrass (Panicum virgatum) used for biofuels production, Loblolly Pine (Pinus taeda) fibers for medium density fiberboard production, and formaldehyde (HCHO) emissions from particleboard were used. Simulations curves (k) of k = 100, k = 1000, and k = 10,000 indicate that the Bonferroni method for detecting special-cause variation is closely aligned with the Shewhart definition of control limits when the pdfs are Gaussian or lognormal.
KW - Control bands
KW - data signatures
KW - near-infrared spectroscopy
KW - Shewhart limits
KW - special-cause variation
UR - https://www.mendeley.com/catalogue/56a9eab7-30ea-3d7d-912f-a33c5e21df6e/
U2 - 10.1080/02664763.2019.1622658
DO - 10.1080/02664763.2019.1622658
M3 - Article
SN - 0266-4763
VL - 46
SP - 3032
EP - 3043
JO - J. Appl. Stat.
JF - J. Appl. Stat.
IS - 16
ER -