论文标题
基于Dempster-Shafer理论的新型多分类器信息融合：应用于基于振动的故障检测
A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection
论文作者
论文摘要
实现高预测率是故障检测的至关重要任务。尽管有各种分类程序可用，但它们都无法在所有应用中都具有很高的精度。因此，在本文中，开发了一种新型的多分类器融合方法，以提高单个分类器的性能。这是通过使用Dempster-Shafer理论（DST）获得的。但是，在证据冲突的情况下，DST可能会产生违反直觉结果。在这方面，设计了基于新指标的预处理技术，以衡量和减轻证据之间的冲突。为了评估和验证所提出方法的有效性，该方法应用于UCI和龙骨的15个基准数据集。此外，它用于根据其宽带振动响应对多晶镍合金涡轮叶片进行分类。通过具有不同噪声水平的统计分析，并通过与四种最先进的融合技术进行比较，这表明所提出的方法提高了分类精度并优于单个分类器。
Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Shafer theory (DST). However, in cases with conflicting evidences, the DST may give counter-intuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it is applied for classifying polycrystalline Nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that that the proposed method improves the classification accuracy and outperforms the individual classifiers.
