Abstract
This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.
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Acknowledgments
This work was financially supported by Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/F021070/1. The experimental equipment was supplied by the Polymer Processing Research Centre, School of Mechanical and Aerospace Engineering, Queen’s University Belfast. The authors would like to thank the staff of the Centre for their time and help, particularly Dr. Gerry McNally, Mr. Alan Clarke, and Mr. Graham Garrett. The support provided by ‘Cherry Pipes Ltd’ is greatly acknowledged.
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Liu, X., Li, K., McAfee, M. et al. Application of nonlinear PCA for fault detection in polymer extrusion processes. Neural Comput & Applic 21, 1141–1148 (2012). https://doi.org/10.1007/s00521-011-0581-y
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DOI: https://doi.org/10.1007/s00521-011-0581-y