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Process monitoring strategy for a steel making shop: a partial least squares regression-based approach

Author

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  • J. Maiti
  • Anupam Das
  • R.N. Banerjee

Abstract

This paper deals with the process monitoring strategy for a Steel Making Shop (SMS). The process and the feedstock characteristics of the SMS were being simultaneously monitored for the detection of an upset condition or an out-of-control situation. Partial Least Squares Regression (PLSR), a multivariate projection-based technique was used for the development of the process representation. Henceforth, T² chart was used to monitor the process and the feedstock characteristics and the out-of-control observations were diagnosed with the aid of contribution plots. Contribution plots revealed the characteristic or the combination of the characteristics responsible for an out-of-control observation. Multivariate Hotelling's T² chart was also used for monitoring of the process and feedstock characteristics and the results thus obtained were compared with that of the PLSR-based T² chart. Data pertaining to the process and feedstock characteristics were collected for a period of six months. The PLSR-based T² chart was able to detect the out-of-control observations and the contribution plots aided in revealing the set of characteristics responsible for the out-of-control observations.

Suggested Citation

  • J. Maiti & Anupam Das & R.N. Banerjee, 2008. "Process monitoring strategy for a steel making shop: a partial least squares regression-based approach," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 3(3), pages 340-359.
    • Handle: RePEc:ids:ijpqma:v:3:y:2008:i:3:p:340-359
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