skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Detecting special-cause variation ‘events’ from process data signatures

Abstract

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.

Authors:
 [1];  [2];  [1];  [3];  [4];  [5]
  1. Center for Renewable Carbon, University of Tennessee, Knoxville, TN, USA
  2. Assured Bio Labs, LLC, Oak Ridge, TN, USA
  3. Holztechnologie und Holzbau, Salzburg University of Applied Sciences, Kuchl, Austria
  4. Center for Renewable Carbon, University of Tennessee, AgResearch, Knoxville, TN, USA
  5. Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
Publication Date:
Research Org.:
Univ. of Tennessee, Knoxville, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office; Cooperative State Research, Education, and Extension Service
OSTI Identifier:
1602599
Alternate Identifier(s):
OSTI ID: 1781079
Grant/Contract Number:  
R11-3215-096; EE0006639; TEN00MS-107
Resource Type:
Published Article
Journal Name:
Journal of Applied Statistics
Additional Journal Information:
Journal Name: Journal of Applied Statistics Journal Volume: 46 Journal Issue: 16; Journal ID: ISSN 0266-4763
Publisher:
Informa UK Limited
Country of Publication:
United Kingdom
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Control bands; Shewhart limits; special-cause variation; data signatures; near-infrared spectroscopy

Citation Formats

Young, Timothy M., Khaliukova, Olga, André, Nicolas, Petutschnigg, Alexander, Rials, Timothy G., and Chen, Chung-Hao. Detecting special-cause variation ‘events’ from process data signatures. United Kingdom: N. p., 2019. Web. https://doi.org/10.1080/02664763.2019.1622658.
Young, Timothy M., Khaliukova, Olga, André, Nicolas, Petutschnigg, Alexander, Rials, Timothy G., & Chen, Chung-Hao. Detecting special-cause variation ‘events’ from process data signatures. United Kingdom. https://doi.org/10.1080/02664763.2019.1622658
Young, Timothy M., Khaliukova, Olga, André, Nicolas, Petutschnigg, Alexander, Rials, Timothy G., and Chen, Chung-Hao. Tue . "Detecting special-cause variation ‘events’ from process data signatures". United Kingdom. https://doi.org/10.1080/02664763.2019.1622658.
@article{osti_1602599,
title = {Detecting special-cause variation ‘events’ from process data signatures},
author = {Young, Timothy M. and Khaliukova, Olga and André, Nicolas and Petutschnigg, Alexander and Rials, Timothy G. and Chen, Chung-Hao},
abstractNote = {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.},
doi = {10.1080/02664763.2019.1622658},
journal = {Journal of Applied Statistics},
number = 16,
volume = 46,
place = {United Kingdom},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1080/02664763.2019.1622658

Save / Share:

Works referenced in this record:

Comparison of Two Wood Plastic Composite Extruders Using Bootstrap Confidence Intervals on Measurements of Sample Failure Data
journal, December 2012


Handling intrinsic non-linearity in near-infrared reflectance spectroscopy
journal, October 1999


Using Control Charts to Monitor Process and Product Quality Profiles
journal, July 2004


Comparing the shapes of regression functions
journal, March 2000


On Probability as a Basis for Action
journal, November 1975


Statistical Process Control for Geometric Specifications: On the Monitoring of Roundness Profiles
journal, January 2008


Bootstrap confidence intervals for smoothing splines and their comparison to bayesian confidence intervals
journal, February 1995

  • Wang, Yuedong.; Wahba, Grace
  • Journal of Statistical Computation and Simulation, Vol. 51, Issue 2-4
  • DOI: 10.1080/00949659508811637

Predicting Key Reliability Response with Limited Response Data
journal, March 2014


Interplay Between Physics and Statistics for Modeling Optical Fiber Bandwidth
journal, August 2002


Monitoring the Process Mean and Variance Using Individual Observations and Variable Sampling Intervals
journal, April 2001


Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal
journal, August 2016

  • Liao, Yongxin; Deschamps, Fernando; Loures, Eduardo de Freitas Rocha
  • International Journal of Production Research, Vol. 55, Issue 12
  • DOI: 10.1080/00207543.2017.1308576

Graphical Comparison of Nonparametric Curves
journal, January 1996

  • Bowman, Adrian; Young, Stuart
  • Applied Statistics, Vol. 45, Issue 1
  • DOI: 10.2307/2986225

On the Monitoring of Linear Profiles
journal, July 2003


Analysis of Variation Transmission in Manufacturing Processes—Part I
journal, April 1999


The Rate of False Signals in Ū Control Charts with Estimated Limits
journal, January 2007


Controversies and Contradictions in Statistical Process Control
journal, October 2000


Quality control decisions with near infrared data
journal, November 2000

  • Sánchez, M. S.; Bertran, E.; Sarabia, L. A.
  • Chemometrics and Intelligent Laboratory Systems, Vol. 53, Issue 1-2
  • DOI: 10.1016/S0169-7439(00)00094-0

The State of Statistical Process Control as We Proceed into the 21st Century
journal, September 2000

  • Stoumbos, Zachary G.; Reynolds, Marion R.; Ryan, Thomas P.
  • Journal of the American Statistical Association, Vol. 95, Issue 451
  • DOI: 10.1080/01621459.2000.10474292

Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study
journal, December 2006

  • Morris, Jeffrey S.; Arroyo, Cassandra; Coull, Brent A.
  • Journal of the American Statistical Association, Vol. 101, Issue 476
  • DOI: 10.1198/016214506000000465