Article: Process monitoring strategy for a steel making shop: a partial least squares regression-based approach Journal: International Journal of Productivity and Quality Management (IJPQM) 2008 Vol.3 No.3 pp.340 - 359 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: J. Maiti, Anupam Das, R.N. Banerjee

Addresses: Department of Industrial Engineering and Management, IIT Kharagpur 721302, India. ' Department of Industrial Engineering and Management, IIT Kharagpur 721302, India. ' Department of Industrial Engineering and Management, IIT Kharagpur 721302, India

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.

Keywords: steel making shops; SMS; process monitoring; partial least squares regression; PLSR; Hotelling; T² chart; contribution plot; process characteristics; feedstock characteristics.

DOI: 10.1504/IJPQM.2008.017503

International Journal of Productivity and Quality Management, 2008 Vol.3 No.3, pp.340 - 359

Published online: 12 Mar 2008 *

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

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