A cloud model-based interval-valued evidence fusion method and its application in fault diagnosis

Xiaobin Xu, Haohao Guo, Zehui Zhang, Shan-en Yu, Leilei Chang, Felix Steyskal, Georg Brunauer

Abstract: In fault diagnosis methods based on evidence fusion, a piece of evidence is usually extracted from the fault feature information, which uses the single-valued belief degrees (SVB) to measure the likelihoods of the different fault modes happening. However, SVB seems rough and incomplete for modeling fault information with uncertainty. Therefore, an interval-valued evidence (IVE) fusion method is proposed via cloud model. Compared to the SVB structure, the IVE with the interval-valued belief structure contains more useful fault information. Firstly, the nominal normal cloud model (NCM) about each fault mode and the test normal cloud model (TNCM) are established through the statistical analysis of the historical and testing fault sample data, respectively. TNCMs are matched with each NCM to obtain the corresponding IVE. Then, a novel IVE reasoning rule is defined to combine all IVEs coming from different fault features. Finally, in the fault diagnosis experiments of motor rotors, the proposed method is verified to provide more accurate fault decision results compared to the traditional evidence fusion method. Furthermore, the proposed method is extended to solve general data classification problems with benchmark datasets of University of California Irvine (UCI), showing it has better classification ability compared to some classification methods.