An ensemble classifier for vibration-based quality monitoring
- URL: http://arxiv.org/abs/2007.08789v2
- Date: Fri, 20 Nov 2020 20:01:51 GMT
- Title: An ensemble classifier for vibration-based quality monitoring
- Authors: Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
- Abstract summary: This paper develops a novel ensemble classifier based on the Dempster-Shafer theory of evidence.
The effectiveness of the proposed framework is validated by its application to 15 UCI and KEEL machine learning datasets.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Vibration-based quality monitoring of manufactured components often employs
pattern recognition methods. Albeit developing several classification methods,
they usually provide high accuracy for specific types of datasets, but not for
general cases. In this paper, this issue has been addressed by developing a
novel ensemble classifier based on the Dempster-Shafer theory of evidence. To
deal with conflicting evidences, three remedies are proposed prior to
combination: (i) selection of proper classifiers by evaluating the relevancy
between the predicted and target outputs, (ii) devising an optimization method
to minimize the distance between the predicted and target outputs, (iii)
utilizing five different weighting factors, including a new one, to enhance the
fusion performance. The effectiveness of the proposed framework is validated by
its application to 15 UCI and KEEL machine learning datasets. It is then
applied to two vibration-based datasets to detect defected samples: one
synthetic dataset generated from the finite element model of a dogbone
cylinder, and one real experimental dataset generated by collecting broadband
vibrational response of polycrystalline Nickel alloy first-stage turbine
blades. The investigation is made through statistical analysis in presence of
different levels of noise-to-signal ratio. Comparing the results with those of
four state-of-the-art fusion techniques reveals the good performance of the
proposed ensemble method.
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