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Margin-based approach for outlier detection of industrial design data using a modified general regression neural network

Published online by Cambridge University Press:  09 February 2022

Jayaram Sivaramakrishnan Open the ORCID record for Jayaram Sivaramakrishnan [Opens in a new window] ,
Gareth Lee ,
David Parlevliet  and
Kok Wai Wong
Jayaram Sivaramakrishnan*
Affiliation:
College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA6150, Australia
Gareth Lee
Affiliation:
College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA6150, Australia
David Parlevliet
Affiliation:
College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA6150, Australia
Kok Wai Wong
Affiliation:
College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA6150, Australia
*
Author for correspondence: Jayaram Sivaramakrishnan, E-mail: j.sivaramakrishnan@murdoch.edu.au, jaya.sivaraman@hotmail.com
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Abstract

The choice of components in industrial design involves setting design parameters that typically must reside inside permissible ranges called “design margins”. This paper proposes a novel automated method called the Margin-Based General Regression Neural Network (MB-GRNN) that classifies design errors for design parameters that are outside of permissible ranges as outliers, directly from industrial design data, using an unsupervised machine learning approach. The method is based on a modified GRNN that estimates extremal margin boundaries of design parameters by self-learning the features from datasets. These extremal permissible margin boundaries are determined by “stretching out” the upper and lower GRNN surfaces using an iterative application of stretch factors (a second kernel weighting factor). The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and lower margin boundaries. These boundaries can then be used to determine outliers and to predict a range of permissible values of design parameters during design. Pushing out extremal margin boundaries reduce the false identification of outliers. This classification technique could be used by industrial engineers to detect likely outliers and to predict a range of permissible output limits for chosen design parameters. The efficacy of this method has been validated against the widespread Parzen window method by comparing experimental results from three multivariate datasets. It was found that the two methods have different but complementary capabilities. The MB-GRNN also uses a modified algorithm for estimating the smoothing parameter using a combination of clustering, k-nearest neighbor, and localized covariance matrix.

Keywords

Decision supportdesign marginGRNNindustrial designmachine learningoutlier detectionParzen window
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e7
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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