Soft sensor design for power measurement and diagnosis in electrical furnace: A parametric estimation approach

In this paper, we propose as a first step a software solution to measure the electrical power consumed in an industrial furnace intended essentially for heat treatments. The soft sensor is constructed from the power physical measurement taken as the output of the set (dimmer + resistances), and the control signal measurement provided by a controller with an unknown structure. The second step consists in a detection of faults like a resistance disconnection, for instance. This phase requires the knowledge of the controller model and the furnace system. An overparametrization method was chosen for the controller estimation. An indirect closed-loop Input-Output (IO) identification approach was used for the furnace model estimation through a Tailor-Made and a decomposition of the closed-loop algorithms. A validation with two other experimental tests concludes the paper.

[1]  Graham C. Goodwin,et al.  A virtual closed loop method for closed loop identification , 2011, Autom..

[2]  Jean-Claude Trigeassou,et al.  Contribution à l'extension de la méthode des moments en automatique : application à l'identification des systèmes linéaires , 1987 .

[3]  I. Landau.  Unbiased recursive identification using model reference adaptive techniques , 1976 .

[4]  I. Landau,et al.  Recursive algorithms for identification in closed loop-a unified approach and evaluation , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[5]  P. V. D. Hof,et al.  Analysis of closed-loop identification with a tailor-made parametrization , 1997 .

[6]  T. Poinot,et al.  Unbiased identification in closed-loop by an output error technique , 1999, 1999 European Control Conference (ECC).

[7]  Thierry Poinot.  Contribution à l'identification des systèmes : - par une méthode de surparamétrisation - en traitement des eaux , 1996 .

[8]  Martin Kozek,et al.  Modeling and Control of an Industrial Continuous Furnace , 2009, 2009 International Conference on Computational Intelligence, Modelling and Simulation.

[9]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[10]  Régis Ouvrard,et al.  On embedded FIR filter models for identifying continuous-time and discrete-time transfer functions: the RPM approach , 2011, Int. J. Control.

[11]  Paul M.J. Van den Hof,et al.  Closed-Loop Issues in System Identification , 1997 .

[12]  A Chéruy,et al.  Software sensors in bioprocess engineering , 1997 .

[13]  A. Abche,et al.  A Pseudo-Output Error Algorithm To Improve Global Convergence , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[14]  Georgi M. Dimirovski,et al.  Industrial furnace control: an experiment in iterative identification and design , 1998 .

[15]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[16]  Thierry Poinot,et al.  Fostering Energy Efficiency in manufacturing plants through economical breakthroughs in power and flow rate measurement , 2012 .

[17]  Olivier Grospeaud.  Contribution à l'identification en boucle fermée par erreur de sortie , 2000 .