Research on multi-task collaborative enhancement matrix machine and its application in fault diagnosis

Abstract Support matrix machine, as an effective classification method, is widely used in single task fault diagnosis. However, for the entire mechanical equipment system, the state information between different components is coupled with each other, and it is difficult to fully express the completion state information of a task by only constructing a diagnostic model for that task. In view of this, this paper proposes a multi-task collaborative enhancement matrix machine (MTCEMM) method. First, a dimension enhancement term is defined, in which nonlinear offsets are used to help the model capture nonlinear relationships in the data, thus improving the information fusion power of the multiple models. Then, the classification hyperplane for different tasks is constructed simultaneously, which completes information sharing among multiple tasks during the construction process, thereby achieving collaborative enhancement of multi-task sample information. Finally, the MTCEMM method is applied to multi-task fault diagnosis of mechanical system, and five evaluation indicators are also selected to display the classification results.