A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

TL;DR

This paper introduces a new multi-classifier fusion method using Dempster-Shafer theory, enhanced by a conflict mitigation technique, which significantly improves fault detection accuracy in vibration-based applications across multiple datasets.

Contribution

The paper proposes a novel fusion approach with a conflict mitigation step based on a new metric, enhancing classifier performance in fault detection tasks.

Findings

Improves classification accuracy over individual classifiers.

Outperforms four state-of-the-art fusion techniques.

Effective across diverse datasets and noise levels.

Abstract

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.