Multiobjective domain intelligent fault diagnosis of rotating machinery based on multitask soft-margin twin matrix machine

Abstract

Support matrix machine (SMM) has emerged as a powerful intelligent classification technique with broad applications in mechanical fault diagnosis. However, SMM and its derivative methods can only complete the recognition of a single-objective task at the same time, making it difficult to balance multitask classification, and easily ignoring information between multiobjectives. Therefore, a multitask failure pattern identification scheme based on multitask soft-margin twin matrix machine (MTSTMM) is proposed. In MTSTMM, a soft-margin is designed to improve the flexibility of handling noisy, and to elevate the model’s out-of-distribution generalization capacity by increasing tolerance for misclassification. Meanwhile, by setting a regularized multitask learning weighting term, matrix singularity during computation and model overfitting during training are avoided, and implicit information between multiple tasks is effectively utilized. In addition, a kernel technique is used to fully utilize the structural information of matrix samples, which effectively solve the problem of slow convergence in traditional matrix classifiers. Through two independent experiments, experimental findings demonstrate that the proposed MTSTMM exhibits stronger multitask classification ability in noisy environments.