Process-Monitoring-for- Quality — A Model Selection Criterion for Shallow Neural Networks

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Published Sep 22, 2019
Carlos A. Escobar Ruben Morales-Menendez

Abstract

Since most manufacturing systems generate only a few
defects per million of opportunities, rare quality event
detection is one of the main applications of process monitoring
for quality. Single-hidden-layer feed-forward
neural networks have been successfully applied to perform
this task. However, since the best network structure
is not known in advance, many models need to be learned
and tested to select a final model with the right number
of hidden neurons. A new three-dimension 3D

How to Cite

Escobar, C. A., & Morales-Menendez, R. (2019). Process-Monitoring-for- Quality — A Model Selection Criterion for Shallow Neural Networks. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.816
Abstract 137 | PDF Downloads 137

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Keywords

Model selection criterion,, Artificial neural networks, Single hidden layer, Binary classification, Highly unbalanced data structures

Section
Technical Papers