Work

A fuzzy cross domain matrix machine for fault diagnosis under multi-objective domain

Year: 2025

Type: article

Abstract: Mechanical fault diagnosis faces significant challenges due to the susceptibility of traditional matrix classifiers to abnormal samples and their inability to leverage cross domain information. To address these limitations, this paper introduces a novel fuzzy cross domain matrix machine (FCDMM), which integrates fuzzy membership modeling with multi-task learning to enhance robustness and enable knowledge transfer across related tasks. FCDMM constructs fuzzy-based decision hyperplanes and designs adaptive boundary factors to mitigate the influence of outliers. Extensive experiments on multiple domain fault datasets demonstrate that FCDMM achieves remarkable accuracy rates of 98.86 % for bearing faults and 99.57 % for gear faults, significantly outperforming state-of-the-art methods.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2025.113368

Authors: Haiyang Pan, Chunan Chen, Tong Chen, Jian Cheng, Jinde Zheng, Shuchao Deng

Institutions: Anhui University of Science and Technology, Anhui University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,200

Funders: National Natural Science Foundation of China, Natural Science Foundation of Anhui Province