Abstract
Detecting non-linear interaction patterns among process variables is an important task for fault detection and propagation analysis. There are many statistical and evolutionary techniques being developed in the literature for prediction of interaction strengths but their accuracy is generally unsatisfactory. This study demonstrates an evolutionary programming approach to uncover non-linear relations among process variables. In this study, we make an attempt to use genetic programming (GP) based approach for this purpose. GP overcomes many shortcomings faced by other statistical or evolutionary techniques in this context. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman process. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.
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Acknowledgments
This work has been conducted by the ESCL Lab at university of Ontario institute of technology (UOIT). The authors would like to thank all the colleagues working the department for their valuable works and cooperation in the field. The authors also thank the reviewers for their helpful suggestions.
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Communicated by G. Acampora.
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Hosseini, A.H., Hussain, S. & Gabbar, H.A. Detecting nonlinear interrelation patterns among process variables using genetic programming. Soft Comput 18, 1283–1292 (2014). https://doi.org/10.1007/s00500-013-1142-3
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DOI: https://doi.org/10.1007/s00500-013-1142-3