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Multivariate Statistical Process Monitoring Strategy for a Steel Making Shop

  • Anupam DasEmail author
Conference paper
  • 55 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Monitoring of a manufacturing process ensures production of consistently good quality end production. In this paper, an attempt has been made to develop a monitoring strategy for a serial multistage manufacturing facility based on multi-block partial least squares regression, a multivariate regression technique. The developed monitoring strategy has been applied to a medium scale steel making shop. The monitoring strategy thus developed was employed for detection as well as for diagnosis of the faults responsible for the poor quality end product. The results obtained were found to be in sync with actual conditions.

Keywords

Hotelling T2 statistic Monitoring chart Multi-block partial least squares regression Process monitoring Steel making shop 

References

  1. 1.
    Ganguly A, Patel SK (2015) Computer-aided design of X and R charts using teaching-learning-based optimization algorithm. Int J Prod Qual Manage 16(3):325–346Google Scholar
  2. 2.
    Doshi JA, Desai DA (2016) Statistical process control an approach for continuous quality improvement in automotive SMEs-Indian case study. Int J Prod Qual Manage 19:387–407Google Scholar
  3. 3.
    Franco BC, Celano G, Castagliola P, Costa AFB (2014) Economic design of Shewhart control charts for monitoring auto correlated data with skip sampling strategies. Int J Prod Econ 151:121–130CrossRefGoogle Scholar
  4. 4.
    Reinikainen S, Hoskuldsson A (2007) Multivariate statistical analysis of a multi-step industrial processes. Anal Chim Acta 595(1/2):248–256CrossRefGoogle Scholar
  5. 5.
    Kourti T (2006) The process analytical technology initiative and multivariate process analysis, monitoring and control. Anal Bioanal Chem 384(5):1043–1048CrossRefGoogle Scholar
  6. 6.
    Han SW, Zhong H (2014) A comparison of MCUSUM-based and MEWMA-based spatiotemporal surveillance under non-homogeneous populations. Qual Reliab Eng Int 31(8):1449–1472CrossRefGoogle Scholar
  7. 7.
    Li G, Hu Y (2018) Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis. Energy Build 173(1):502–515CrossRefGoogle Scholar
  8. 8.
    Botre C, Mansouri M, Karim MN, Nounou H, Nounou M (2017) Multiscale PLS based GLRT for fault detection of chemical processes. J Loss Prev Process Ind 46:143–153Google Scholar
  9. 9.
    Moreira SA, Sarraguça J, Saraiva DF, Carvalho R, Lopes JA (2015) Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production. Fuel 150:697–704CrossRefGoogle Scholar
  10. 10.
    Doble P, Sandercock M, Du Pasquier E, Petocz P, Roux C, Dawson M (2003) Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks. Forensic Sci Int 132(1):26–39CrossRefGoogle Scholar
  11. 11.
    Choi SW, Lee I (2005) Multiblock PLS-based localized process diagnosis. J Process Control 15(3):295–306CrossRefGoogle Scholar
  12. 12.
    Kourti T, Nomikos P, MacGregor JF (1995) Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. J Process Control 5(4):277–284CrossRefGoogle Scholar
  13. 13.
    Tupkary RH, Tupkary VR (1998) An introduction to modern steel making, 6th edn. Khanna Publishers, New DelhiGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology PatnaPatnaIndia

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