摘要: Prognostics and systems health management (PHM) have been manifested the pivotal role for guaranteeing the on-orbit service capacity of space equipment with high reliability, high security and maintainability. Considering the limi... 展开 Prognostics and systems health management (PHM) have been manifested the pivotal role for guaranteeing the on-orbit service capacity of space equipment with high reliability, high security and maintainability. Considering the limitation of spatial information bandwidth and initial information, few shot learning based incremental fault diagnosis provides a new way for on-line fault detection of on-orbit equipment. In this paper, an incremental fault diagnosis method for on-orbit equipment based on cut edge weight estimation is proposed by combining the framework of collaborative learning and the idea of graph theory data editing. The experiment of applying above method to space actuating system was also carried out. 收起