Multiple restriction cross-border matrix machine for multiple objective fault diagnosis

As a single-task classification method, support matrix machine (SMM) is widely used in mechanical equipment fault diagnosis. However, when diagnosing multi-objective tasks, it is difficult to fully utilize the common information between multiple tasks, resulting in limited information contained in the model. Meanwhile, SMM is extremely sensitive to abnormal samples, which is not conducive to model construction. To address the aforementioned issues, a novel multiple restriction cross-border matrix machine (MRCBMM) is proposed. In MRCBMM, a weighted constraint group (WCG) is firstly designed to adjust the influence of different abnormal samples on the hyperplane, thereby determining the optimal position of the hyperplane. Meanwhile, MRCBMM defines a matrix kernel expansion (MKE) that maps matrix samples to high-dimensional space to fully utilize the structural information of the original signal. In addition, to achieve cross-border diagnosis of MRCBMM, a regularized multi-task learning framework is constructed to complete the features and parameters. Two sets of multi-objective rotating mechanical fault datasets are used for validation, and the results show that MRCBMM improves diagnostic accuracy by approximately 2% in scenarios containing abnormal samples compared with existing methods, and consistently achieves over 98% accuracy on clean datasets.