An ensemble classifier for vibration-based quality monitoring

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. In the proposed procedure, prior to DST combination, three steps should be taken: (i) selection of proper classifiers by maximizing the joint mutual information between predicted and target outputs, (ii) optimal redistribution of the classifiers' outputs by considering the distance between the predicted and target outputs, (iii) utilizing five different weighting factors to enhance the fusion performance. The effectiveness of the proposed framework is validated by its application to 13 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 noise levels. Comparing the results with those of five state-of-the-art fusion techniques reveals the good performance of the proposed ensemble method.

Automated summary

This paper introduces a novel ensemble classifier based on the Dempster-Shafer theory of evidence, aiming to improve fusion performance through several steps and validating its effectiveness by applying to multiple datasets and vibration-based datasets.