Fiber Bragg grating strain gauges are usually used to access the structural strain and monitor the health of the structure. However， the performance of the structural health monitoring system is dependent on the status of such a large number of sensors， which are quite easy to break down after a long-term service in various conditions. Based on the phenomenon that similar characteristics will be observed for the outpout of the fiber grating strain gauge in the same structure， we propose a novel Malfunction-diagnosis method for the sensors in the fiber Bragg grating strain gauges. The proposed method extracted the eigenvalue s （i.e.， sample data length， standard deviation， energy value and principal component period） using signal processing algorithm. The eigenvalue convergence center points were determined by loop iterations. The eigenvalue distances from the convergence centers were standardized and merged into a comprehensive index for the health monitoring of the sensors. The simulation proves that the malfunction points can be effectively identified when the number of malfunction points is below 20% of the total points. The proposed method was used to monitor the 416 fiber Bragg grating strain gauges of five-hundred-meter aperture spherical telescope （FAST） health monitoring system， the results showed that the eigenvalue lists of 317 sensors could be extracted and 4 malfunction points and 14 abnormal points could be identified. Instead of a large amount of training with prior knowledge， the proposed method could provide reliable malfunction diagnosis for the sensors in fiber Bragg grating strain gauges just based on the characteristics of the data， which might be practical useful for the health monitoring of the FAST structure.