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 article, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster–Shafer theory. However, in cases with conflicting evidences, the Dempster–Shafer theory may give counterintuitive 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. Furthermore, 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 the proposed method improves the classification accuracy and outperforms the individual classifiers.

实现高预测率是故障检测中的关键任务。尽管可以使用各种分类程序，但是它们都不能在所有应用中提供高精度。因此，在本文中，开发了一种新颖的多分类器融合方法以提高各个分类器的性能。这是通过使用Dempster–Shafer理论获得的。但是，在证据矛盾的情况下，Dempster-Shafer理论可能会得出与直觉相反的结果。在这方面，设计了一种基于新指标的预处理技术，以测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，该方法被应用于UCI和KEEL的15个基准数据集。此外，根据其宽带振动响应，可将其用于多晶镍合金一级涡轮叶片的分类。通过对不同噪声水平的统计分析，并与四种最新的融合技术进行比较，结果表明，该方法提高了分类精度，并且优于单个分类器。