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An ensemble classifier for vibration-based quality monitoring
Mechanical Systems and Signal Processing ( IF 8.4 ) Pub Date : 2021-08-26 , DOI: 10.1016/j.ymssp.2021.108341
Vahid Yaghoubi 1, 2 , Liangliang Cheng 1, 2 , Wim Van Paepegem 1 , Mathias Kersemans 1
Affiliation  

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.



中文翻译:

用于基于振动的质量监控的集成分类器

制造部件的基于振动的质量监控通常采用模式识别方法。尽管开发了几种分类方法,但它们通常为特定类型的数据集提供高精度,但不适用于一般情况。在本文中,通过开发基于 Dempster-Shafer 证据理论的新型集成分类器解决了这个问题。在所提出的程序中,在 DST 组合之前,应采取三个步骤:(i)通过最大化预测输出和目标输出之间的联合互信息来选择合适的分类器,(ii)通过考虑之间的距离来优化分类器输出的重新分配预测和目标输出,(iii)利用五个不同的加权因子来提高融合性能。所提出框架的有效性通过其在 13 个 UCI 和 KEEL 机器学习数据集上的应用得到了验证。然后将其应用于两个基于振动的数据集以检测有缺陷的样本:一个由狗骨圆柱体有限元模型生成的合成数据集,以及一个通过收集多晶镍合金第一级涡轮叶片的宽带振动响应生成的真实实验数据集。调查是通过统计分析在不同噪音水平下进行的。将结果与五种最先进的融合技术的结果进行比较,揭示了所提出的集成方法的良好性能。一个由狗骨圆柱体有限元模型生成的合成数据集,以及一个通过收集多晶镍合金第一级涡轮叶片的宽带振动响应生成的真实实验数据集。调查是通过统计分析在不同噪音水平下进行的。将结果与五种最先进的融合技术的结果进行比较,揭示了所提出的集成方法的良好性能。一个由狗骨圆柱体有限元模型生成的合成数据集,以及一个通过收集多晶镍合金第一级涡轮叶片的宽带振动响应生成的真实实验数据集。调查是通过统计分析在不同噪音水平下进行的。将结果与五种最先进的融合技术的结果进行比较,揭示了所提出的集成方法的良好性能。

更新日期:2021-08-26
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