A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection
This work provides an incremental improvement in classification accuracy for fault detection, particularly relevant for applications like turbine blade health monitoring.
This paper addresses the challenge of achieving high prediction rates in fault detection by developing a novel multi-classifier fusion approach based on Dempster-Shafer theory (DST). The method incorporates a preprocessing technique to mitigate conflicting evidence within DST, and when applied to 15 benchmark datasets and turbine blade classification, it improves classification accuracy compared to individual classifiers and four state-of-the-art fusion techniques.
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.