A Dynamic Fault Diagnosis Method for Gravitational Reference Sensor Based on Informer-RS

Gravitational reference sensor (GRS) used in the space gravitational wave detection mission may encounter intermittent failures characterized by random data jumps during its operation, potentially disrupting the overall mission progress. This article first analyzes the pose sensing principle of the GRS and provides the differential capacitance fault model. In response to this type of fault, a data-driven dynamic fault diagnosis approach based on a novel informer model is proposed. In this design, a random slice (RS) mechanism is introduced into the informer model to enhance the robustness, and an anomaly-correcting temporal prediction method is used to mitigate the influence of abnormal sequences in prediction. Then, considering the fault characteristics of the GRS, an improved isolation forest (IF) algorithm is proposed to calculate anomaly scores for detected sequences, and dynamic thresholds are established based on these scores for fault diagnosis. Finally, comparative experiments based on actual engineering data are conducted to verify the prediction performance of the method, and a series of simulation experiments based on Monte Carlo statistical analysis are conducted to evaluate the fault diagnosis capability of the proposed approach. The results indicate that the fault detection accuracy of this approach can reach 97.83%, and it outperforms some common approaches in terms of diagnostic performance and robustness.