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

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. • A fuzzy cross domain matrix machine (FCDMM) is proposed. • A fuzzy cross-domain term is designed to quantify the fuzzy membership relationships of category sample points and construct decision hyperplanes based on fuzzy logic to achieve nonlinear modeling and optimization of data distribution. • A pair of boundary factors are introduced to control margin errors, thereby reducing the sensitivity of the model to abnormal samples.