Work

Composite Multiscale Fractional Fuzzy Diversity Entropy and Its Application in Bearing Fault Identification

Year: 2026

Type: article

Abstract: This paper presents an intelligent fault identification approach integrating composite multiscale fractional fuzzy diversity entropy (CMFFDE) for feature extraction, joint mutual information (JMI) for feature selection, and an extreme learning machine (ELM) for classification. First, the CMFFDE method is developed by incorporating composite multiscale analysis into the proposed fractional fuzzy diversity entropy to extract multiscale fault characteristics. JMI feature selection is then applied to identify sensitive features, which are subsequently used as input to the ELM classifier for fault identification. The effectiveness and superiority of the proposed approach are verified using bearing experimental data. Analysis results demonstrate that the proposed approach achieves better identification performance in bearing vibration signal analysis than alternative methods.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10040243

Authors: Xiong Gan, Guangyou Yang

Institution: Hubei University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: Hubei University of Technology, Hubei University